Systems and methods for mitigating in-phase and quadrature mismatch

ABSTRACT

A method of optimizing at least one IQMC parameter value for an IQMC includes: generating a set of tested IQMC candidate parameter values by performing an iterative method including selecting a first IQMC candidate parameter value for the at least one parameter of the IQMC; determining, using the first IQMC candidate parameter value, a performance metric value that comprises at least one of (i) an image rejection ratio (IRR) value, (ii) a signal-to-interference-plus-noise ratio (SINR) value, or (iii) a signal-to-image ratio (SImR) value; and determining a second IQMC candidate parameter value that is an update to the first IQMC candidate parameter value. The method of optimizing at least one IQMC parameter value for an IQMC further includes determining an IQMC candidate parameter value of the set of tested IQMC candidate parameter values that optimizes the performance metric.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on and, under 35 U.S.C. § 119(e), claimspriority to U.S. Provisional Patent Application No. 63/025,983, filed onMay 15, 2020 in the United States Patent and Trademark Office. Theentire contents of U.S. Provisional Patent Application No. 63/025,983are incorporated herein by reference.

FIELD

The present disclosure is generally related to communication systems ordevices. In particular, the present disclosure is related to systems andmethods for increasing a performance of communication systems or devicesthat may exhibit an in-phase (I) and quadrature (Q) mismatch. The I andQ mismatch may be referred to herein as “IQMM.”

BACKGROUND

An imbalance between I and Q branches of quadrature transmitters (TXs)or receivers (RXs) can create interference between the certainfrequencies after up-conversion or down-conversion. The IQMM may becaused by non-ideal characteristics of I and Q paths, and may degradethe TX or RX performance by reducing an effective signal to interferenceratio. Hence, compensation of IQMM for the TX and/or RX of thequadrature transceivers can be helpful.

SUMMARY

According to some embodiments, a method of optimizing at least onein-phase (I) and quadrature (Q) mismatch compensator (MC) parametervalue for at least one IQMC parameter of an IQMC includes: generating aset of tested IQMC candidate parameter values by performing an iterativemethod until an exit condition is reached, and determining an IQMCcandidate parameter value of the set of tested IQMC candidate parametervalues that optimizes the performance metric. At least one iteration ofthe iterative method includes: selecting a first IQMC candidateparameter value for the at least one parameter of the IQMC; determining,using the first IQMC candidate parameter value, a performance metricvalue that comprises at least one of (i) an image rejection ratio (IRR)value, (ii) a signal-to-interference-plus-noise ratio (SINR) value, or(iii) a signal-to-image ratio (SImR) value; and determining a secondIQMC candidate parameter value that is an update to the first IQMCcandidate parameter value. The set of tested IQMC test parametersincludes at least the first IQMC candidate parameter value and thesecond IQMC candidate parameter value.

According to some embodiments, a device configured to optimize at leastone IQMC parameter value for at least one IQMC parameter of an IQMCincludes a processor, and non-transitory processor-executable mediastoring instructions that, when executed by the processor, cause theprocessor to: generate a set of tested IQMC candidate parameter valuesby performing an iterative method until an exit condition is reached,and determine an IQMC candidate parameter value of the set of testedIQMC candidate parameter values that optimizes the performance metric.At least one iteration of the iterative method includes: selecting afirst IQMC candidate parameter value for the at least one parameter ofthe IQMC; determining, using the first IQMC candidate parameter value, aperformance metric value that comprises at least one of (i) an imagerejection ratio (IRR) value, (ii) a signal-to-interference-plus-noiseratio (SINR) value, or (iii) a signal-to-image ratio (SImR) value; anddetermining a second IQMC candidate parameter value that is an update tothe first IQMC candidate parameter value. The set of tested IQMC testparameters includes at least the first IQMC candidate parameter valueand the second IQMC candidate parameter value.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects, features, and advantages of certain embodiments of the presentdisclosure will be readily apparent from the following detaileddescription and the accompanying drawings.

FIG. 1A shows a diagram of at least a portion of a quadraturetransmitter, according to some embodiments.

FIG. 1B shows a diagram of at least a portion of a quadrature receiver,according to some embodiments.

FIG. 2A shows a diagram of a complex valued pre-compensator (CVC) for aquadrature transmitter, according to some embodiments.

FIG. 2B shows a diagram of a complex valued pre-compensator (CVC) for aquadrature transmitter, according to some embodiments.

FIG. 2C shows a diagram of a real valued pre-compensator (RVC) for aquadrature transmitter, according to some embodiments.

FIG. 2D shows a diagram of a complex valued compensator (CVC) for aquadrature receiver, according to some embodiments.

FIG. 2E shows a diagram of a real valued compensator (RVC) for aquadrature receiver, according to some embodiments.

FIG. 3 shows a flowchart of an IQ mismatch compensator (IQMC) parameteroptimization method, according to some embodiments.

FIG. 4 shows a flowchart of an IQMC parameter optimization method,according to some embodiments.

FIG. 5 shows a diagram of an electronic device in a network environment,according to some embodiments.

DETAILED DESCRIPTION

The image rejection ratio (IRR) can be an IQMC performance metric, andmay be defined as a function of (i) a signal level produced by thewanted input frequency and (ii) a signal level produced by the unwantedimage frequency. The function may be, or may involve or include, a ratioof those two quantities. Certain embodiments described herein involveone or more techniques (e.g., including an iterative technique, amachine learning technique, a gradient ascent or gradient descenttechnique, or other technique) that can be used to obtain IQMCparameters that improve (e.g., maximize) an IRR of a TX or of an RX. Forexample, the present disclosure provides one or more solutions using agradient ascent (or descent) search approach to solve an IRRoptimization problem and provides for finding a gradient of an IRRrelated cost function in closed form as a function of IQ impairmentparameters and IQMC parameters for compensator architectures in TX andRX.

The IRR of the TX or RX can be frequency dependent, and the IRR that isoptimized or maximized (via, at least in part, selection or setting ofTX or RX of IQMC parameters) may be a smallest IRR of a set of IRRsrespectively corresponding to a set of frequencies (e.g., correspondingto a desired frequency band). In some embodiments, the IRR that ismaximized may be another IRR (e.g., may be a mean or median IRR of theset of IRRs).

Signal-to-interference-plus-noise ratio (SINR) can be an IQMCperformance metric and may be defined as a function of (i) a power of asignal of interest, (ii) an interference power (from one or more (e.g.,all) interfering signals), and (iii) a power of a background noise. Byway of example, the SINR may be defined as a function of the power ofthe signal of interest and an aggregation of (e.g., addition of) theinterference power and the power of the background noise. By way of amore a specific example, the SINR may be a function of (e.g., may beequal to) a ratio of (i) the power of the signal of interest and (ii)the addition of the interference power and the power of the backgroundnoise. For example, the present disclosure provides one or moresolutions using a gradient ascent (or descent) search approach to solvean SINR optimization problem and provides for finding a gradient of anSINR related cost function in closed form as a function of IQ impairmentparameters and IQMC parameters for compensator architectures in TX andRX.

The SINR of the TX or RX can be frequency dependent, and the SINR thatis maximized (via, at least in part, selection or setting of TX or RX ofIQMC parameters) may be a smallest SINR of a set of SINRs respectivelycorresponding to a set of frequencies (e.g., corresponding to a desiredfrequency band). In some embodiments, the SINR that is maximized may beanother SINR (e.g., may be a mean or median SINR of the set of SINRs).

Signal-to-image ratio (SImR) can be an IQMC performance metric and maybe defined as a function of (i) a signal level at a desired frequency to(ii) a signal level at an interfering frequency (e.g., the imagefrequency). The function may be indicative of a relative strength of thesignal level at the desired frequency and the signal level at theinterfering frequency (e.g., may be a ratio), or may be indicative of anabsolute strength of the signal level at the desired frequency and thesignal level at the interfering frequency. Certain embodiments describedherein involve one or more techniques (e.g., including an iterativetechnique, a machine learning technique, a gradient ascent or gradientdescent technique, or other technique) that can be used to obtain IQMCparameters that improve (e.g., maximize) an SImR of a TX or of an RX.For example, the present disclosure provides one or more solutions usinga gradient ascent (or descent) search approach to solve an SImRoptimization problem and provides for finding a gradient of an SImRrelated cost function in closed form as a function of IQ impairmentparameters and IQMC parameters for compensator architectures in TX andRX.

The SImR of the TX or RX can be frequency dependent, and the SImR thatis maximized (via, at least in part, selection or setting of TX or RX ofIQMC parameters) may be a smallest SImR of a set of SImRs respectivelycorresponding to a set of frequencies (e.g., corresponding to a desiredfrequency band). In some embodiments, the SImR that is maximized may beanother SImR (e.g., may be a mean or median SImR of the set of SImRs).

Herein, certain references to, and/or description of, at least one ofthe performance metrics discussed above (IRR, SINR, and SImR) may beapplicable to the other performance metrics, as may be apparent from thecontext.

Certain techniques described herein provide for optimizing IQMCparameters for an IQMC compensator, or for determining or generatingoptimized IQMC parameters for an IQMC compensator. The terms optimizingor optimized, as used herein, can refer to improving or improved. Forexample, optimizing an IRR value, an SINR value, or an SImR value canrefer to increasing the IRR value, the SINR value, or the SImR value. Anoptimized IRR value, SINR value, or SImR value can refer to a largestIRR value, SINR value, or SImR value of a set of IRR values, SINRvalues, or SImR values (e.g., a set of tested values, default values, orother reference values).

FIG. 1A shows a diagram of at least a portion of a TX 100, according tosome embodiments. Note that although the example TX 100 depicted in FIG.1A is a zero-intermediate frequency (IF) TX, in other embodiments, theTX may be implemented as a non-zero IF TX, and the techniques describedherein can still be applied. FIG. 1A also depicts an I and Q signaltransmission path in the TX 100. The TX 100 may be a stand-alone TX, ormay be included in a transceiver device or system, or any otherappropriate communication device or system. The TX 100 includes one ormore of a TX IQMC 102, a digital-to-analog converter (DAC) 104, a TXup-converter 106, and a signal aggregator 108.

As a brief overview, and as shown by the arrows depicted in FIG. 1A, anin-phase signal s_(I)[n] (where “n” is a time index—note that as usedherein, certain lower case symbols may be used to indicate a function ina time domain, and certain upper case symbols may be used to indicate afunction in a frequency domain) that is, e.g., intended fortransmission, is input to the TX IQMC 102. A quadrature signal s_(Q)[n](that is, e.g., intended for transmission) is also input to the TX IQMC102. The TX IQMC 102 processes the s_(I)[n] signal and the s_(Q)[n]signal, and responsively outputs a compensated signal u_(I)[n] to a DAC104, and responsively outputs a compensated signal u_(Q)[n] to a DAC104. Note that u_(I)[n] can be a function of both s_(I)[n] and s_(Q)[n],and u_(Q)[n] can be a function of both s_(I)[n] and s_(Q)[n].

The DAC 104 converts the compensated signal u_(I)[n] to an analog Isignal, and outputs the analog I signal to the TX up-converter 106. TheTX up-converter 106 up-converts the analog I signal and outputs theup-converted analog I signal to the signal aggregator 108. Note thatthroughout this specification, the s_(I)[n] signal, the u_(I)[n] signal,the analog I signal, and the up-converted analog I signal may bereferred to as an I signal.

The DAC 104 converts the compensated signal u_(Q)[n] to an analog Qsignal, and outputs the analog Q signal to the TX up-converter 106. TheTX up-converter 106 up-converts the analog Q signal and outputs theup-converted analog Q signal to the signal aggregator 108. Note thatthroughout this specification, the s_(Q)[n] signal, the u_(Q)[n] signal,the analog Q signal, and the up-converted analog Q signal may bereferred to as a Q signal.

The signal aggregator 108 is configured to aggregate (e.g., add) theanalog I signal and the analog Q signal to generate an upconvertedsignal Z that is appropriate for transmission. The signal Z is labeledZ_(RF,TX)(t) in FIG. 1A to indicate that it is a signal that varies inthe time domain and that is output by a TX at a radio frequency (RF).

Referring to the TX up-converter 106, the TX up-converter is configuredto up-convert signals from a baseband frequency to an RF frequency moreappropriate for transmission. The TX up-converter 106 includes a 106 a,a filter 106 b, a mixer 106 c, a mixer 106 d, a TX local oscillator (LO)106 e, and a phase shifter 106 f.

The filter 106 a may be any appropriate filter, such as an analog,baseband, and/or low-pass filter. The filter 106 a may be an analogbaseband (ABB) filter. The filter 106 a may have an impulse responserepresented by h_(ITX)(t). The filter 106 b may be similar to the filter106 a, and may have similar features, characteristics, structure, and/orfunctionality as the filter 106 a. The filter 106 b may have an impulseresponse represented by h_(QTX)(t). A mismatch between the impulseresponses h_(ITX)(t) and h_(QTX)(t) (h_(ITX)(t)≠h_(QTX)(t)) on the I andQ paths of the TX 100 may create frequency-dependent IQ mismatch(FD-IQMM). Such mismatches may degrade the IRR, the SINR, and/or theSImR of the TX 100 (e.g., as discussed in detail herein).

The mixer 106 c may be any appropriate mixer for a TX up-converter. Ingeneral, a mixer may perform operations on input signals, such asmultiplying two input signals. The mixer 106 d may be similar to themixer 106 c, and may have any of the features, characteristics,structure, and/or functionality as the mixer 106 c.

The TX LO 106 e supplies a signal to the mixer 106 c, and the signal 106c can be expressed as cos(ω_(L0) _(TX) t), where ω_(L0) _(TX) is anangular frequency of the TX LO 106 e. The TX LO 106 e also supplies asignal (ideally, the same or a similar signal as the signal that the TXLO 106 e supplies to mixer 106 c) to the phase shifter 106 f, whichshifts (e.g., delays) the phase by 90° or approximately 90° and suppliesthe delayed signal to the mixer 106 d. Thus, the mixer 106 d may ideallybe supplied with a signal that is the same as, or similar to, the signalsupplied to the mixer 106 c, but shifted by 90° or approximately 90°.However, certain non-ideal mismatches may occur in practice that causethe signals actually supplied to the mixers 106 c and 106 d (e.g., bythe TX LO 106 e and/or the phase shifter 106 f) to deviate from thisideal, or certain non-ideal characteristics of the mixers may cause thesignals to be processed differently from an ideal mixing, and at leastsome of these non-ideal mismatches or characteristics may be expressedas g_(TX) (mismatch gain) and ϕ_(TX) (mismatch phase offset) in thefollowing formula which represents the signal that is input to the mixer106 d:

−g _(TX) sin(ω_(L0) _(TX) t+ϕ _(TX))  (1).

When g_(TX)≠1 and/or ϕ_(TX)≠0, there is a frequency-independent (FI) IQmismatch in the TX 100. Such mismatches may degrade the IRR, the SINR,and/or the SImR of the TX 100 (e.g., as discussed in detail herein).

The frequency response of the baseband equivalent of the upconvertedsignal in the TX path (at the output of mixers) in frequency-domain isgiven by

Z _(TX)(f)=G _(1TX)(f)U(f)+G _(2TX)(f)U*(−f)+N _(TX)(f),  (2)

where U(f) is the frequency response of the baseband TX signal,N_(TX)(f) represents additive TX noise, and G_(1TX)(f) and G_(2TX)(f)are defined as

$\begin{matrix}{{{G_{1\;{TX}}(f)} = \frac{{H_{ITX}(f)} + {g_{TX}e^{j\;\phi_{TX}}{H_{QTX}(f)}}}{2}},{{G_{2\;{TX}}(f)} = \frac{{H_{ITX}(f)} - {g_{TX}e^{j\;\phi_{TX}}{H_{QTX}(f)}}}{2}},} & (3)\end{matrix}$

where H_(ITX)(f) and H_(QTX)(f) in (3) denote the frequency responses ofin-phase filter 106 a and quadrature filter 106 b, respectively. Withoutany IQMM (g_(TX)=1, ϕ_(TX)=0, and H_(ITX)(f)=H_(QTX)(f)), G_(2TX)(f) andconsequently the second term in (2) become zero. Without any IQMC, IRRcan then be expressed as (by way of example):

$\begin{matrix}{{{{IRR}_{TX}(f)} = \frac{{{G_{1\;{TX}}(f)}}^{2}}{{{G_{2\;{TX}}(f)}}^{2}}},} & (4)\end{matrix}$

which becomes infinite in case of no IQMM since G_(2TX)(f)=0. However,when G_(2TX)(f) is non-zero and when there is some IQMM, the IRR isfinite. Other IQMC performance parameters such as SINR and SImR maysimilarly be large without any IQMM, and may be small when G_(2TX)(f) isnon-zero and when there is some IQMM.

The TX IQMC 102 may compensate for IQMM, including the IQ mismatchesdescribed above, thereby increasing the IRR, the SINR, and/or the SImRof the TX 100. The TX IQMC 102 may include, for example, one or more ofa CVC IQMC (e.g., the TX CVC IQMC 202 shown in FIG. 2A or the TX CVCIQMC 203 shown in FIG. 2B), or an RVC IQMC (e.g., the TX RVC IQMC 204shown in FIG. 2C). The TX IQMC 102 may include one or more components,certain examples of which are described below at least in reference toFIG. 2A, FIG. 2B, and FIG. 2C. Certain parameters of those components(or certain parameters of the IQMC itself) may be referred to herein asIQMC parameters, or IQMC coefficients. The present disclosure providesfor determining, selecting, setting, and/or updating IQMC parameters forthe TX IQMC 102 (e.g., using an iterative technique such as the IQMCparameter optimization method 300 shown in FIG. 3) such that an IRR, andSINR, and/or an SImR of the TX 100 is optimized.

FIG. 1B shows a diagram of at least a portion of an RX 110, according tosome embodiments. Note that although the example RX 110 depicted in FIG.1B is a zero-IF RX, in other embodiments, the RX may be implemented as anon-zero IF RX, and the techniques described herein can still beapplied. FIG. 1B also depicts an I and Q signal reception path in the RX110. The RX 110 may be a stand-alone RX, or may be included in atransceiver device or system, or any other appropriate communicationdevice or system. The RX 110 includes one or more of an RX IQMC 112, ananalog-to-digital converter (ADC) 114, and an RX down-converter 116.

As a brief overview, and as shown by the arrows depicted in FIG. 1B, asignal Z is received by the RX 110, and is input to the RXdown-converter 116. The signal Z is labeled Z_(RF, RX)(t) in FIG. 1B toindicate that it is a signal that varies in the time domain and that isinput to an RX at an RF frequency. The RX down-converter 116down-converts the signal Z and outputs an analog I signal and an analogQ signal, which are respectively sent to ADCs 114. One of the ADCs 114converts the analog I signal to a digital I signal r_(I)[n], and anotherof the ADCs 114 converts the analog Q signal to a digital Q signalr_(Q)[n]. The signal r_(I)[n] and the signal r_(Q)[n] are sent to the RXIQMC 112, which compensates for RX IQ mismatch, thereby generating acompensated I signal y_(I)[n] and a compensated Q signal y_(Q)[n], whichmay be further processed or used by the RX 110 or by a connectedcomponent or device. The compensated I signal y_(I)[n] may be a functionof one or both of the digital I signal r_(I)[n] and the digital Q signalr_(Q)[n]. The compensated Q signal y_(Q)[n] may be a function of one orboth of the digital I signal r_(I)[n] and the digital Q signal r_(Q)[n].

Referring to the RX down-converter 116 in more detail, the RXdown-converter 116 is configured to down-convert signals from an RFfrequency to a baseband frequency. The RX down-converter 116 includes afilter 116 a, a filter 116 b, a mixer 116 c, a mixer 116 d, a TX localoscillator (LO) 116 e, and a phase shifter 116 f.

The mixer 116 c may be any appropriate mixer for an RX down-converter.As mentioned above, in general, a mixer may perform operations on inputsignals, such as multiplying two input signals. The mixer 116 c mayreceive the Z signal and a signal from the RX LO 116 e, and may mix themto generate an output that is sent to the filter 116 a. The mixer 116 dmay receive the Z signal and a signal from the RX LO 116 e (e.g., viathe phase shifter 116 f), and may mix them to generate an output that issent to the filter 116 b. The mixer 116 d may be similar to the mixer116 c, and may have any of the features, characteristics, structure,and/or functionality as the mixer 116 c.

The RX LO 116 e supplies a signal to the mixer 116 c, and the signal canbe expressed as: 2 cos(ω_(L0) _(RX) t), where ω_(L0) _(RX) is an angularfrequency of the RX LO 116 e. The RX LO 116 e also supplies a signal(ideally, the same or a similar signal as the signal that the RX LO 116e supplies to mixer 116 c) to the phase shifter 116 f, which shifts(e.g., delays) the phase by 90° or approximately 90° and supplies theshifted signal to the mixer 116 d. Thus, the mixer 116 d may ideally besupplied with a signal that is the same as, or similar to, the signalsupplied to the mixer 116 c, but shifted by 90° or approximately 90°.However, certain non-ideal mismatches may occur in practice that causethe signals actually supplied to the mixers 116 c and 116 d (e.g., bythe TX LO 106 e and/or the phase shifter 106 f) to deviate from thisideal, or certain non-ideal characteristics of the mixers may cause thesignals to be processed differently from an ideal mixing, and at leastsome of the non-ideal mismatches may be expressed as g_(TX) (mismatchgain) and ϕ_(TX) (mismatch phase offset) in the following formula, whichrepresents the signal that is input to the mixer 106 d:

−2g _(RX) sin(ω_(L0) _(RX) t+ϕ _(RX))  (5).

When g_(RX)≠1 and/or ϕ_(RX)≠0, there is a frequency-independent (FI) IQmismatch in the RX 110. Such mismatches may degrade the IRR, the SINR,and/or the SImR of the RX 110 (e.g., as discussed in detail below).

The filter 116 a may be any appropriate filter, such as an analog,baseband, and/or low-pass filter. The filter 116 a may receive, as aninput, an output of the mixer 116 c. The filter 116 a may have animpulse response represented by h_(IRX)(t). The filter 116 b may besimilar to the filter 116 a, and may have any of the features,characteristics, structure, and/or functionality as the filter 116 a.The filter 116 b may receive, as an input, an output of the mixer 116 d.The filter 116 b may have an impulse response represented by h_(QRX)(t).A mismatch between the impulse responses h_(IRX)(t) and h_(QRX)(t)(h_(IRX)(t)≠h_(QRX)(t)) on the I and Q paths of the RX 110 may createfrequency-dependent IQ mismatch (FD-IQMM). Such mismatches may degradethe IRR, the SINR, and/or the SImR of the RX 110 (e.g., as discussed indetail herein).

By way of example, a frequency response of the received baseband signalmay be expressed as:

R(f)=G _(1RX)(f)Z _(RX)(f)+G _(2RX)(f)Z* _(RX)(−f)+N _(RX)(f),  (6)

where R(f) is the frequency response of the received baseband signal,Z_(RX)(f) is a frequency response of the baseband equivalent of thereceived signal at the input of the mixers, N_(RX)(f) is additive RXnoise, and G_(1RX)(f) and G_(2RX)(f) are defined as

$\begin{matrix}{{{G_{1\;{RX}}(f)} = \frac{{H_{IRX}(f)} + {g_{RX}e^{{- j}\;\phi_{RX}}{H_{QRX}(f)}}}{2}},{{G_{2\;{RX}}(f)} = \frac{{H_{IRX}(f)} - {g_{RX}e^{{+ j}\;\phi_{RX}}{H_{QRX}(f)}}}{2}},} & (7)\end{matrix}$

where H_(IRX)(f) and H_(QRX)(f) in (7) denote the frequency responses ofin-phase filter 116 a and quadrature filter 116 b, respectively. Thesecond term in (6) represents the interfering image signal due to RXIQMM.

The RX IQMC 112 may compensate for IQ mismatches, including the IQmismatches discussed above. The RX IQMC 112 may include, for example, aCVC IQMC (e.g., the RX CVC IQMC 206 shown in FIG. 2D), or an RVC IQMC(e.g., the RX RVC IQMC 208 shown in FIG. 2E). The RX IQMC 112 mayinclude one or more components, certain examples of which are describedbelow at least in reference to FIG. 2D and FIG. 2E. Certain parametersof those components (or certain parameters of the IQMC itself) may bereferred to herein as IQMC parameters, or IQMC coefficients. The presentdisclosure provides for determining, selecting, setting, and/or updatingIQMC parameters for the RX IQMC 112 (e.g., using an iterative techniquesuch as the IQMC parameter optimization method 300 shown in FIG. 3) suchthat an IRR, an SINR, and/or an SImR of the RX 110 is improved.

Referring now to FIGS. 2A-2E, certain IQMC configurations are describedin accordance with certain embodiments. These devices, methods, andtechniques disclosed herein are not necessarily limited to the specificIQMC configurations shown in FIGS. 2A-2E, and other IQMC configurationsmay be implemented as appropriate.

FIG. 2A shows a TX CVC IQMC 202, according to some embodiments. The TXCVC IQMC 202 (which may be referred to herein as an IQMC 202 forbrevity) may include a delay component 202 a configured to implement adelay of T_(D), a complex-conjugate operator 202 b, a complex-valuedfilter 202 c (e.g., a finite impulse response (FIR) filter), and asignal aggregator 202 d. The IQMC 202 may receive a signal s[n] (e.g.,an in-phase signal s_(I)[n] and/or a quadrature signal s_(Q)[n] as shownin FIG. 1A). The received signal s[n]=s_(I)[n]+js_(Q)[n] goes throughthe delay component 202 a in a first path of the IQMC 202. In a secondpath of the IQMC 202, the received signal s[n] goes through thecomplex-conjugate operator 202 b (which is configured to output thecomplex conjugate of an input) and then through the complex-valuedfilter 202 c. The complex-valued filter 202 c may have L tap weightsw_(TX,i), where i indexes the taps, and may have an impulse responsew_(TX)[n]=Σ_(i=0) ^(L-1)w_(TX,i)δ[n−i].

The outputs of both paths are aggregated (e.g., added) by the aggregator202 d, and the aggregator 202 d outputs a compensated signal u[n]. Thecompensated signal u[n] may include the signal u_(I)[n] and/or thesignal u_(Q)[n] shown in FIG. 1A.

The IQMC 202 may have certain IQMC parameters, including the L tapweights of the complex-valued filter 202 c. The IQMC parameters of theIQMC 202 may be determined, configured and/or set using techniquesdescribed herein (e.g., the method shown in FIG. 3) to increase ormaximize an IRR, and SINR, and/or an SImR of the TX 100 shown in FIG.1A. Once the IQMC parameters are determined, the IQMC 202 can beconfigured to implement those parameters. Such implementation can bedone at the time of manufacture or in the field, and in someembodiments, can be done dynamically (e.g., according to a maintenanceschedule, or based on a condition of, or feedback from, a transmitter ortransceiver that incorporates the IQMC 202).

The following provides an example of a general mathematical model for asignal in a TX 100 that is processed by a TX IQMC 102. The followingwill also apply this general mathematical model to certain exampleIQMCs, including the IQMC 202.

We can write the frequency response of the pre-compensated signal in theTX 100, U(f) (used above, e.g., in equation 2), as a function of a cleanbaseband signal using an IQMC, as follows:

U(f)=F _(1TX)(f)S(f)+F _(2TX)(f)S*(−f),  (8)

where, F_(1TX)(f) and F_(2TX)(f) are a function of IQMC parametersand/or characteristics. By substituting equation 8 into equation 2, thebaseband equivalent of the TX up-converted signal will be

Z _(TX)(f)=S(f)(F _(1TX)(f)G _(1TX)(f)+F* _(2TX)(−f)G_(2TX)(f))+S*(−f)(F _(2TX)(f)G _(1TX)(f)+F* _(1TX)(−f)G _(2TX)(f))+N_(TX)(f).  (9)

For the IQMC 202, F_(1TX)(f)=e^(−j2πfT) ^(D) , and F_(2TX)(f)=W_(TX)(f),where W_(TX)(f) may denote the frequency response of filter w_(TX)[n].

The following provides an example of a mathematical model for IRR in aTX 100. One example formula for modelling the IRR of a TX 100 is asfollows:

$\begin{matrix}{{{{IRR}_{TX}(f)} = \frac{{{{{F_{1{TX}}(f)}{G_{1{TX}}(f)}} + {{F_{2{TX}}^{*}\left( {- f} \right)}{G_{2{TX}}(f)}}}}^{2}}{{{{{F_{2{TX}}(f)}{G_{1{TX}}(f)}} + {{F_{1{TX}}^{*}\left( {- f} \right)}{G_{2{TX}}(f)}}}}^{2}}},} & (10)\end{matrix}$

Using the above equation 10 for modelling the IRR of a TX 100, and theabove expressions for F_(1TX)(f) and F_(2TX)(f) for the IQMC 202, onecan mathematically model the IRR for the IQMC 202. Using techniquesdescribed herein, such an IRR can be optimized.

The following provides an example of a mathematical model for asignal-to-interference-plus-noise ratio (SINR) in a TX 100. One exampleformula for modelling the SINR of a TX 100 is as follows:

$\begin{matrix}{{{{SINR}_{TX}(f)} = \frac{{{{{F_{1{TX}}(f)}{G_{1{TX}}(f)}} + {{F_{2{TX}}^{*}\left( {- f} \right)}{G_{2{TX}}(f)}}}}^{2}}{{{{{F_{2{TX}}(f)}{G_{1{TX}}(f)}} + {{F_{1{TX}}^{*}\left( {- f} \right)}{G_{2{TX}}(f)}}}}^{2} + \sigma_{TX}^{2}}},} & (11)\end{matrix}$

where σ_(TX) ² is a normalized TX noise variance, e.g.

$\sigma_{TX}^{2} = {\frac{{\mathbb{E}}\left( {{N_{TX}(f)}}^{2} \right)}{{\mathbb{E}}\left( {{S(f)}}^{2} \right)}.}$

One can determine IQMC coefficients by maximizing or increasing SINR,which considers both IQMM and noise effects. When noise power issignificant (e.g., comparable to that of the image signal level, orgreater), maximizing SINR may provide significant improvement in termsof the system throughput.

In some of the above derivations, we assumed that G_(1TX)(f), G_(2TX)(f)at frequencies f₁, . . . , f_(K) are known (e.g., they are estimated bysending pilot signals at continuous time baseband frequencies f₁, . . ., f_(K)). However, one might have estimates of the relative mismatches,and not have reasonable estimates of G_(1TX)(f), G_(2TX)(f)independently. The following provides some techniques that can be usedwhen estimates of relative mismatches are known, even if one does nothave reasonable estimates of G_(1TX)(f), G_(2TX)(f) independently.

Define the following parameter that is a function of gain and filtermismatches:

$\begin{matrix}{{V_{TX}(f)}\overset{\Delta}{=}\frac{H_{ITX}(f)}{g_{TX}{H_{QTX}(f)}}} & (12)\end{matrix}$

Given this, the IRR can be estimated as follows:

$\begin{matrix}{{{{IR}{R_{TX}(f)}} = \frac{{{{{V_{TX}(f)}e^{{- j}\;\phi_{TX}}} + 1 + {\left( {{{V_{TX}(f)}e^{{- j}\;\phi_{TX}}} - 1} \right)e^{{+ j}2\pi fT_{D}}{W_{TX}^{*}\left( {- f} \right)}}}}^{2}}{{{{{V_{TX}(f)}e^{{- j}\;\phi_{TX}}} - 1 + {\left( {{{V_{TX}(f)}e^{{- j}\;\phi_{TX}}} + 1} \right)e^{{+ j}2\pi fT_{D}}{W_{TX}(f)}}}}^{2}}},} & (13)\end{matrix}$

Assuming that ϕ_(TX) and V_(TX)(f) are known (e.g., estimated, such asby sending pilot signals at continuous time baseband frequencies f₁, . .. , f_(K)), improved IQMC coefficients can be determined using iterativetechniques described herein (e.g., in reference to FIGS. 3 and 4).

Some applications involve determining an SImR instead of, or in additionto, determining an IRR or an SINR. Like the IRR or the SINR, the SImRmay be indicative of a performance or quality of a TX IQMC. One examplemanner of expressing SImR for a TX is as follows:

$\begin{matrix}{{{SImR}_{TX}(f)} = {\frac{{{{{F_{1{TX}}(f)}{G_{1{TX}}(f)}} + {{F_{2{TX}}^{*}\left( {- f} \right)}{G_{2{TX}}(f)}}}}^{2}}{{{{{F_{2{TX}}\left( {- f} \right)}{G_{1TX}\left( {- f} \right)}} + {{F_{1{TX}}^{*}(f)}{G_{2{TX}}\left( {- f} \right)}}}}^{2}}.}} & (14)\end{matrix}$

Techniques presented herein can be used to determine and/or implementIQMC parameters, such as the tap weights that define w_(TX)[n] (tapweights of the complex-valued filter 202 c) or that define W_(TX)(f) (afrequency domain response or Fourier transform (e.g., fast Fouriertransform or other Fourier transform) of w_(TX)[n]), that improve,increase, or maximize IRR, SINR, or SImR.

FIG. 2B shows a TX CVC IQMC 203, according to some embodiments. The TXCVC IQMC 203 may be referred to herein as an IQMC 203, for brevity. TheIQMC 203 may include a delay component 203 a configured to implement adelay of T_(D), a complex-valued filter 203 b (e.g., a finite impulseresponse (FIR) filter), a real-value operator 203 c (configured to passa real value portion of a signal, and block an imaginary portion of asignal), and a signal aggregator 203 d. The IQMC 203 may receive asignal s[n] (e.g., an in-phase signal s_(I)[n] and/or a quadraturesignal s_(Q)[n] as shown in FIG. 1A). The received signals[n]=s_(I)[n]+js_(Q)[n] goes through the delay component 203 a in afirst path of the IQMC 203. In a second path of the IQMC 203, thereceived signal s[n] goes through the complex-valued filter 203 b. Thecomplex-valued filter 203 b may have L tap weights w_(TX,i), where iindexes the taps, and may have an impulse response w_(TX)[n]=Σ_(i=0)^(L-1)w_(RX,i)δ[n−i]. The output of the complex-valued filter 203 b issent to the real-value operator 203 c, which passes a real valuecomponent of the signal to the aggregator 203 d and blocks an imaginarycomponent of the signal. The outputs of both paths are aggregated (e.g.,added) by the aggregator 203 d, and the aggregator 203 d outputs acompensated signal u[n]. The compensated signal u[n] may include thesignal s_(I)[n] and/or the signal s_(Q)[n] shown in FIG. 1A.

The IQMC 203 may have certain IQMC parameters, including the L tapweights of the complex-valued filter 203 b. The IQMC parameters of theIQMC 203 may be determined, configured and/or set using techniquesdescribed herein (e.g., the method shown in FIG. 3) to increase ormaximize an IRR, and SINR, and/or an SImR of the TX 100 shown in FIG.1A. Once the IQMC parameters are determined, the IQMC 203 can beconfigured to implement those parameters. Such implementation can bedone at the time of manufacture or in the field, and in someembodiments, can be done dynamically (e.g., according to a maintenanceschedule, or based on a condition of, or feedback from, a transmitter ortransceiver that incorporates the IQMC 203).

Referring to equations 8 and 9, for the IQMC 203,

${{F_{1TX}(f)} = {e^{{- j}2\pi fT_{D}} + \frac{W_{TX}(f)}{2}}},{and}$${{F_{2TX}(f)} = \frac{W_{TX}^{*}\left( {- f} \right)}{2}},$

where W_(TX)(f) may denote the frequency response of filter w_(TX)[n].This, in combination with equation 10, provides an example of amathematical model for IRR for a TX 100 that includes the IQMC 203.Additionally, this, in combination with equation 11, provides an exampleof a mathematical model for SINR for a TX 100 that includes the IQMC203. Additionally, the above expressions for F_(1TX)(f) and F_(2TX)(f)in combination with equation 14 provides an example of a mathematicalmodel for SImR for a TX 100 that includes the IQMC 203.

Given the above, the following provides an example of a mathematicalmodel for IRR in a TX 100 that includes the IQMC 203 (where V_(TX)(f) isdefined as in equation 12):

$\begin{matrix}{{{IR}{R_{TX}(f)}} = {\frac{{{1 + \frac{e^{{+ j}\;\phi_{TX}}}{V_{TX}(f)} + {e^{{+ j}2\pi\;{fT}_{D}}{W_{TX}(f)}}}}^{2}}{\left| {1 - \frac{e^{{+ j}\;\phi_{TX}}}{V_{TX}(f)} + {e^{{+ j}2\pi\; f\; T_{D}}{W_{TX}^{*}\left( {- f} \right)}}} \right|^{2}}.}} & (15)\end{matrix}$

Techniques presented herein can be used to determine and/or implementIQMC parameters, such as the tap weights that define w_(TX)[n] or thetap weights that define W_(TX)(f) (a frequency domain response orFourier transform (e.g., fast Fourier transform or other Fouriertransform) of w_(TX)[n]), that improve, increase, or maximize the IRR,the SINR, and/or the SImR.

FIG. 2C shows a TX RVC IQMC 204, according to some embodiments. The TXRVC IQMC 204 may be referred to herein as an IQMC 204, for brevity. TheIQMC 204 shown in FIG. 2C may include a delay component 204 a configuredto provide a delay T_(D), a multiplier 204 b configure to multiply atleast one input based on a multiplication factor, an aggregator 204 cconfigured to aggregate (e.g., add) at least two inputs, and areal-valued filter 204 d (e.g., a FIR filter). The delay component 204 amay receive a digitized input signal s_(I)[n], and may introduce a delayT_(D) to generate a compensated signal u_(I)[n]. The input signals_(I)[n] may be input to the multiplier 204 b, which multiplies theinput signal s_(I)[n] based on a real-valued cross-multiplication factorα_(TX) that is input to the multiplier 204 b. The multiplied signal isinput to the aggregator 204 c, and an input signal s_(Q)[n] is alsoinput to the aggregator 204 c. The aggregator 204 c aggregates (e.g.,adds) the multiplied signal and the input signal s_(Q)[n], and outputsthe resultant signal to the real-valued filter 204 d. The real-valuedfilter 204 d may have L tap weights d_(TX,i), where i indexes the taps,and may have an impulse response d_(TX,i)[n]=Σ_(i=0)^(L-1)d_(TX,i)δ[n−i]. The real-valued filter 204 d outputs a compensatedsignal u_(Q)[n].

The IQMC 204 may have certain IQMC parameters, including the real-valuedcross-multiplication factor α_(TX) that is input to the multiplier 204b, and the L tap weights of the real-valued filter 204 d. The IQMCparameters of the IQMC 204 may be determined, configured and/or setusing techniques described herein (e.g., the method shown in FIG. 3) toincrease or maximize an IRR, an SINR, and/or an SImR of the TX 100 shownin FIG. 1A. Once the IQMC parameters are determined, the IQMC 204 can beconfigured to implement those parameters. Such implementation can bedone at the time of manufacture or in the field, and in someembodiments, can be done dynamically (e.g., according to a maintenanceschedule, or based on a condition of, or feedback from, a transmitter ortransceiver that incorporates the IQMC 204).

Referring to equations 8 and 9, for the IQMC 204,

${{F_{1TX}(f)} = {\frac{e^{{- j}2\pi fT_{D}}}{2} + \frac{{D_{TX}(f)}\left( {1 + {j\;\alpha_{TX}}} \right)}{2}}},{and}$${{F_{2TX}(f)} = {\frac{e^{{- j}2\pi fT_{D}}}{2} - \frac{{D_{TX}(f)}\left( {1 - {j\alpha_{TX}}} \right)}{2}}},$

where D_(TX)(f) may denote the frequency response of filter d_(TX)[n].This, in combination with equation 10, provides an example of amathematical model for IRR for a TX 100 that includes the IQMC 204.Additionally, this, in combination with equation 11, provides an exampleof a mathematical model for SINR for a TX 100 that includes the IQMC204. Additionally, the above expressions for F_(1TX)(f) and F_(2TX)(f)in combination with equation 14 provides an example of a mathematicalmodel for SImR for a TX 100 that includes the IQMC 204.

Furthermore, the following provides an example of a mathematical modelfor IRR in a TX 100 that includes the IQMC 204 (where V_(TX)(f) isdefined as in equation 12):

$\begin{matrix}{{{IR}{R_{TX}(f)}} = {\frac{{{{{V_{TX}(f)}e^{{- j}\;\phi_{TX}}} + {e^{{+ j}2\pi fT_{D}}{D_{TX}(f)}\left( {{j\;\alpha_{TX}} + 1} \right)}}}^{2}}{{{{{V_{TX}(f)}e^{{- j}\;\phi_{TX}}} + {e^{{+ j}2\pi fT_{D}}{D_{TX}(f)}\left( {{j\;\alpha_{TX}} - 1} \right)}}}^{2}}.}} & (16)\end{matrix}$

Techniques presented herein can be used to determine and/or implementIQMC parameters, such as the tap weights that define d_(TX)[n] or thetap weights that define D_(TX)(f) (a frequency domain response orFourier transform (e.g., fast Fourier transform or other Fouriertransform) of d_(TX)[n]), and/or α_(TX), that improve, increase, ormaximize the IRR, the SINR, and/or the SImR.

FIG. 2D shows an RX CVC IQMC 206, according to some embodiments. The RXCVC IQMC 206 may be referred to herein as an IQMC 206, for brevity. TheIQMC 206 may include a delay component 206 a configured to implement adelay of T_(D), a real-value operator 206 b (configured to pass a realvalue portion of a signal, and block an imaginary portion of a signal),a complex-valued filter 206 c (e.g., a finite impulse response (FIR)filter), and a signal aggregator 206 d. The IQMC 206 may receive asignal r[n] (e.g., an in-phase signal r_(I)[n] and/or a quadraturesignal r_(Q)[n] as shown in FIG. 1B). The received signalr[n]=r_(I)[n]+jr_(Q)[n] goes through the delay component 206 a in afirst path of the IQMC 206. In a second path of the IQMC 206, thereceived signal r[n] goes through the real-value operator 206 b, whichpasses a real value component of an input signal and blocks an imaginarycomponent of the input signal. The output of the real-value operator 206b is then sent to the complex-valued filter 206 c. The complex-valuedfilter 206 c may have L tap weights w_(RX,i), where i indexes the taps,and may have an impulse response w_(RX)[n]=Σ_(i=0) ^(L-1)w_(RX,i)δ[n−i].The outputs of both paths are aggregated (e.g., added) by the aggregator206 d, and the aggregator 206 d outputs a compensated signal y[n]. Thecompensated signal y[n] may include the signal y_(I)[n] and/or thesignal y_(Q)[n] shown in FIG. 1B.

The IQMC 206 may have certain IQMC parameters, including the L tapweights of the complex-valued filter 206 c. The IQMC parameters of theIQMC 206 may be determined, configured and/or set using techniquesdescribed herein (e.g., the method shown in FIG. 3) to increase ormaximize an IRR, and SINR, and/or an SImR of the RX 200 shown in FIG.1B. Once the IQMC parameters are determined, the IQMC 206 can beconfigured to implement those parameters. Such implementation can bedone at the time of manufacture or in the field, and in someembodiments, can be done dynamically (e.g., according to a maintenanceschedule, or based on a condition of, or feedback from, a receiver ortransceiver that incorporates the IQMC 206).

The following provides an example of a general mathematical model for asignal in an RX 110 that is processed by an RX IQMC 112. The followingwill also apply this general mathematical model to certain exampleIQMCs, including the IQMC 206.

We can write the frequency response of the compensated signal in the RX110, using an IQMC, as follows:

Y(f)=F _(1RX)(f)R(f)+F _(2RX)(f)R*(−f).  (17)

where, F_(1RX)(f) and F_(2RX)(f) are a function of IQMC parametersand/or characteristics. By using equation 17 and equation 6, thecompensated RX signal will be

Y(f)=Z _(RX)(f)(F _(1RX)(f)G _(1RX)(f)+F _(2RX)(f)G* _(RX)(−f))+Z*_(RX)(−f)(F _(1RX)(f)G _(2RX)(f)+F _(2RX)(f)G* _(1RX)(−f))+F _(1RX)(f)N_(RX) +F _(2RX)(f)N* _(RX)(−f),  (18).

For the IQMC 206,

${{F_{1RX}(f)} = {e^{{- j}2\pi fT_{D}} + \frac{W_{RX}(f)}{2}}},{{{and}\mspace{14mu}{F_{2{RX}}(f)}} = \frac{W_{RX}(f)}{2}},$

where W_(RX)(f) may denote the frequency response of filter w_(RX)[n].

The following provides an example of a mathematical model for IRR in anRX 110. One example formula for modelling the IRR of an RX 110 is asfollows:

$\begin{matrix}{{{{IR}{R_{RX}(f)}} = \frac{{{{{F_{1{RX}}(f)}{G_{1{RX}}(f)}} + {{F_{2{RX}}(f)}{G_{2{RX}}^{*}\left( {- f} \right)}}}}^{2}}{{{{{F_{1{RX}}(f)}{G_{2{RX}}(f)}} + {{F_{2{RX}}(f)}{G_{1{RX}}^{*}\left( {- f} \right)}}}}^{2}}},,} & (19)\end{matrix}$

Using the above equation 19 for modelling the IRR of an RX 110, and theabove expressions for F_(1TX)(f) and F_(2TX)(f) for the IQMC 206, onecan mathematically model the IRR for the IQMC 206. Using techniquesdescribed herein, such an IRR can be optimized.

The following provides an example of a mathematical model for asignal-to-interference-plus-noise ratio (SINR) in an RX 110. One exampleformula for modelling the SINR of an RX 110 is as follows:

$\begin{matrix}{{{{SINR}_{RX}(f)} = \frac{{{{{F_{1RX}(f)}{G_{1RX}(f)}} + {{F_{2RX}(f)}{G_{2RX}^{*}\left( {- f} \right)}}}}^{2}}{\begin{matrix}{{{{F_{1R{X{(f)}}}{G_{2RX}(f)}} + {{F_{2RX}(f)}{G_{1{RX}}^{*}\left( {- f} \right)}}}}^{2} +} \\{\sigma_{RX}^{2}\left( {{{F_{1RX}(f)}}^{2} + {{F_{2RX}(f)}}^{2}} \right)}\end{matrix}}},} & (20)\end{matrix}$

where σ_(RX) ² is a normalized RX noise variance, e.g.

$\sigma_{RX}^{2} = {\frac{{\mathbb{E}}\left( {{N_{RX}(f)}}^{2} \right)}{{\mathbb{E}}\left( {{Z_{RX}(f)}}^{2} \right)}.}$

One can determine IQMC coefficients by maximizing or increasing SINR,which considers both IQMM and noise effects. When noise power issignificant (e.g., comparable to that of the image signal level, orgreater), maximizing SINR may provide significant improvement in termsof the system throughput. Using the above formula 20 in combination withthe above expressions for F_(1RX)(f) and F_(2RX)(f) for the IQMC 206,one can mathematically model the SINR for the RX 110 having the IQMC206.

In some of the above derivations, we assumed that G_(1RX)(f), G_(2RX)(f)at frequencies f₁, . . . , f_(K) are known (e.g., they are estimated bysending pilot signals at continuous time baseband frequencies f₁, . . ., f_(K)). However, one might have estimates of the relative mismatches,and not have reasonable estimates of G_(1RX)(f), G_(2RX)(f)independently. The following provides some techniques that can be usedwhen estimates of relative mismatches are known, even if one does nothave reasonable estimates of G_(1RX)(f), G_(2RX)(f) independently.

The following provides an example of a mathematical model for IRR in anRX 110. Define the following parameter that is a function of gain andfilter mismatches:

$\begin{matrix}{{V_{RX}(f)}\overset{\Delta}{=}\frac{H_{IRX}(f)}{g_{RX}{H_{QRX}(f)}}} & (21)\end{matrix}$

One example formula for modelling the IRR of an RX 110 that includes theRX CVC IQMC 206 is as follows:

$\begin{matrix}{{{IRR}_{RX}(f)} = {\frac{{{1 + \frac{e^{{- j}\;\phi_{RX}}}{V_{RX}(f)} + {e^{{+ j}2\pi fT_{D}}{W_{RX}(f)}}}}^{2}}{{{1 - \frac{e^{{+ j}\;\phi_{RX}}}{V_{RX}^{*}\left( {- f} \right)} + {e^{{+ j}2\pi fT_{D}}{W_{RX}(f)}}}}^{2}}.}} & (22)\end{matrix}$

Some applications involve determining an SImR instead of, or in additionto, determining an IRR or an SINR. Like the IRR or the SINR, the SImRmay be indicative of a performance or quality of an RX IQMC. One examplemanner of expressing SImR for an RX is as follows:

$\begin{matrix}{{{{SImR}_{RX}(f)} = \frac{{{{{F_{1RX}(f)}{G_{1RX}(f)}} + {{F_{2RX}(f)}{G_{2RX}^{*}\left( {- f} \right)}}}}^{2}}{{{{{F_{1RX}\left( {- f} \right)}{G_{2RX}\left( {- f} \right)}} + {{F_{2RX}\left( {- f} \right)}{G_{1{RX}}^{*}(f)}}}}^{2}}},.} & (23)\end{matrix}$

Techniques presented herein can be used to determine and/or implementIQMC parameters, such as the tap weights that define w_(RX)[n] (tapweights of the complex-valued filter 206 c) or that define W_(RX)(f) (afrequency domain response or Fourier transform (e.g., fast Fouriertransform or other Fourier transform) of w_(RX)[n]), that improve,increase, or maximize the IRR, the SINR, and/or the SImR.

FIG. 2E shows an RX RVC IQMC 208, according to some embodiments. The RXRVC IQMC 208 may be referred to herein as an IQMC 208, for brevity. TheIQMC 208 shown in FIG. 2E may include a delay component 208 a configuredto provide a delay T_(D), a multiplier 208 b configure to multiply atleast one input based on a multiplication factor, an aggregator 208 cconfigured to aggregate (e.g., add) at least two inputs, and areal-valued filter 208 d (e.g., a FIR filter). The delay component 208 amay receive a digitized input signal r_(I)[n], and may introduce a delayT_(D) to generate a compensated signal y_(I)[n]. The compensated signaly_(I)[n] may be input to the multiplier 208 b, which multiplies thecompensated signal y_(I)[n] based on a real-valued cross-multiplicationfactor α_(RX) that is input to the multiplier 208 b. The multipliedsignal is input to the aggregator 208 c. An input signal r_(Q)[n] isinput to the real-valued filter 208 d. The real-valued filter 208 d mayhave L tap weights d_(TX,i), where i indexes the taps, and may have animpulse response d_(RX,i)[n]=Σ_(i=0) ^(L-1)d_(RX,i)δ[n−i]. Thereal-valued filter 208 d outputs a filtered signal to the aggregator 208c. The aggregator 208 c aggregates (e.g., adds) the multiplied signaland the filtered signal, and outputs the resultant signal r_(Q)[n].

The IQMC 208 may have certain IQMC parameters, including the real-valuedcross-multiplication factor α_(RX) that is input to the multiplier 208b, and the L tap weights of the real-valued filter 208 d. The IQMCparameters of the IQMC 208 may be determined, configured and/or setusing techniques described herein (e.g., the method shown in FIG. 3) toincrease or maximize an IRR of the RX 110 shown in FIG. 1B. Once theIQMC parameters are determined, the IQMC 208 can be configured toimplement those parameters. Such implementation can be done at the timeof manufacture or in the field, and in some embodiments, can be donedynamically (e.g., according to a maintenance schedule, or based on acondition of, or feedback from, a receiver or transceiver thatincorporates the IQMC 208).

For the IQMC 208,

${{{F_{1RX}(f)} = {\frac{e^{{- j}2\pi fT_{D}}\left( {1 + {j\alpha_{RX}}} \right)}{2} + \frac{D_{RX}(f)}{2}}},{and}}\mspace{14mu}$${{F_{2RX}(f)} = {\frac{e^{{- j}2\pi fT_{D}}\left( {1 + {j\alpha_{RX}}} \right)}{2} - \frac{D_{RX}(f)}{2}}},$

where D_(RX)(f) may denote the frequency response of filter d_(RX)[n].

Using the above equation 19 for modelling the IRR of an RX 110, and theabove expressions for F_(1TX)(f) and F_(2TX)(f) for the IQMC 208, onecan mathematically model the IRR for the IQMC 208. Using techniquesdescribed herein, such an IRR can be optimized.

In some of the above derivations, we assumed that G_(1RX)(f), G_(2RX)(f)at frequencies f₁, . . . , f_(K) are known (e.g., they are estimated bysending pilot signals at continuous time baseband frequencies f₁, . . ., f_(K)). However, one might have estimates of the relative mismatches,and not have reasonable estimates of G_(1RX)(f), G_(2RX)(f)independently. The following provides some techniques that can be usedwhen estimates of relative mismatches are known, even if one does nothave reasonable estimates of G_(1RX)(f), G_(2RX)(f) independently.

The following provides an example of a mathematical model for IRR in anRX 110. One example formula for modelling the IRR of an RX 110 thatincludes the RX RVC IQMC 208 is as follows (where V_(RX)(f) is definedas in equation 21):

$\begin{matrix}{{{IRR}_{RX}(f)} = {\frac{{{{{V_{RX}(f)}e^{{+ j}\;\phi_{RX}}} + {e^{{+ j}\; 2\;\pi\; f\; T_{D}}\frac{D_{RX}(f)}{{j\;\alpha_{RX}} + 1}}}}^{2}}{{{{{V_{RX}^{*}\left( {- f} \right)}e^{{- j}\;\phi_{RX}}} - {e^{{+ j}\; 2\;\pi\; f\; T_{D}}\frac{D_{RX}(f)}{{j\;\alpha_{RX}} + 1}}}}^{2}}.}} & (24)\end{matrix}$

Techniques presented herein can be used to determine and/or implementIQMC parameters, such as the tap weights that define d_(RX)[n] (tapweights of the real-value filter 208 d) or the tap weights that defineD_(RX)(f) (a frequency domain response or Fourier transform (e.g., fastFourier transform or other Fourier transform) of d_(RX)[n]), and/orα_(RX), that improve, increase, or maximize IRR.

Furthermore, one can mathematically model the SINR for the RX 110 havingthe IQMC 208, using the formula 20 in combination with the aboveexpressions for F_(1RX)(f) and F_(2RX)(f) for the IQMC 208.

Additionally, some applications involve determining an SImR instead of,or in addition to, determining an IRR or an SINR. Like the IRR or theSINR, the SImR may be indicative of a performance or quality of an RXIQMC. One example manner of expressing SImR for an RX is as shown inEquation 23. Using that equation, and the above expressions forF_(1TX)(f) and F_(2TX)(f) for the IQMC 208, one can mathematically modelthe SImR of the RX that includes the IQMC 208.

FIG. 3 shows an IQMC parameter optimization method 300, according tosome embodiments. The IQMC parameter optimization method 300 can beperformed by a computing device that includes a communication device,such as a device that includes a TX and/or an RX, or that includes anIQMC compensator being optimized or tested. The IQMC parameteroptimization method 300 can also be performed by a computing device thatdoes not include the IQMC compensator being optimized or tested.

As a brief overview, the depicted IQMC parameter optimization method 300includes setting an initial IQMC parameter value (302), determining aperformance metric value using the IQMC parameter value (304), anddetermining whether to perform another iteration (306). If at operation(306) the computing device determines to perform another iteration, theIQMC parameter optimization method 300 proceeds to operation (308) whichincludes updating the IQMC parameter value, and returning to operation(304). Otherwise, the IQMC parameter optimization method 300 proceeds todetermining an optimal IQMC parameter value of a set of tested IQMCparameter values (310).

The IQMC parameter optimization method 300 is an iterative techniquethat provides for determining IQMC parameters that increase or maximizea performance metric, such an IRR, a SINR, and/or an SImR of a TX or RXchain. The iterative technique can include, for example, a gradientascent or gradient descent technique (such as that the example shown inFIG. 4), or another appropriate iterative technique, such as a Newton'sMethod technique.

Generally speaking, IRR, SINR, and SImR can be frequency-dependent, andthe techniques described herein may be implemented to determine or setIQMC parameters that increase or maximize an IRR, SINR, or SImR of a TXor RX chain for a particular frequency, for a particular set offrequencies, and/or for a function of one or more frequencies (e.g., anaverage IRR, SINR, or SImR over a set of frequencies). For example, thetechniques may be used to determine or set IQMC parameters that increaseor maximize a lowest IRR of a set of IRRs that correspond to a set offrequencies of interest, such as the frequencies of a particular channelbeing used by the TX, RX, or transceiver. The lowest IRR is often (butnot always) found at edges of a channel rather than in a middle portionof the channel (e.g., at an edge corresponding to 5% or less, 10% orless, or 15% or less of a total frequency band of the channel), and theIQMC parameters may be selected to increase or maximize an IRR of one ormore frequencies corresponding to such an edge. In other embodiments, adifferent criteria may be used to select an IRR (corresponding to aparticular frequency) to be increased or maximized. For example, theiterative technique may be used to determine or set IQMC parameters thatincrease or maximize a minimum geometric mean of IRR per componentcarrier (CC) in a carrier aggregation (CA) scheme. SINR and SImR can beoptimized in a similar manner as described above for IRR.

In FIG. 3, at operation 302, the computing device sets an initial IQMCparameter value. The IQMC parameter value may be any appropriate IQMCparameter value for any appropriate IQMC parameter, including any of theIQMC parameters discussed herein, such as tap weights for a filter of aTX or RX IQMC and/or a real-valued cross-multiplication factor (e.g.,when optimizing IQMC parameters for an RVC). The initial IQMC parametervalue can be selected in any appropriate manner, such as, for example,by optimizing on the assumption of no IQMM.

At operation 304, the computing device determines a performance metricvalue using at least one candidate parameter value (e.g., using theinitial IQMC parameter value or using an updated IQMC parameter value).Note that in some embodiments, a candidate parameter value is selectedfor one IQMC parameter, and in other embodiments, a respective candidateparameter value is selected for each of a plurality of IQMC parameters.The performance metric can include an IRR, and SINR, and/or an SImR fora communication device that includes the IQMC compensator for which atleast one parameter is being optimized. The performance metric value canbe estimated using a real or simulated IQMC compensator that implementsor utilizes the candidate parameter value.

In some embodiments, the IQMC parameter optimization method 300 is aniterative gradient ascent or gradient descent method, and at (304) thecomputing device determines a performance metric value and a gradient ofthe performance metric using the IQMC parameters. The performance metricvalue can be calculated, for example, as described herein. Certainvalues, such as F_(1TX/RX)(f) and F_(2TX/RX)(f) or ϕ_(TX/RX) andV_(TX/RX)(f) that may be used in calculating the performance metricvalues, are IQMM parameters at TX and RX chains that may be knownapriori or may be estimated, for example, by sending pilot signals(e.g., at continuous time baseband frequencies ±f₁, . . . , ±f_(K))through the chain, and using recorded results of such propagation toestimate F_(1TX/RX)(f) and F_(2TX/RX)(f) or ϕ_(TX/RX) and V_(TX/RX)(f).In some embodiments one may assume that one already has estimates ofF_(1TX/RX)(f) and F_(2TX/RX)(f) or ϕ_(TX/RX) and V_(TX/RX)(f) atfrequencies ±f₁, . . . , ±f_(K), and can use them to obtain IQMCparameter values.

The gradient of the performance metric can be calculated using anyappropriate technique, such as the technique described below withrespect to FIG. 4. In some embodiments, an iterative technique is usedto determine or set IQMC parameter values that optimize a minimumgeometric mean of IRR per CC in a CA scheme, and a calculation of thegradient may include converting the performance metric to a logarithmicscale and using a regular mean.

At operation 306, the computing device determines whether to performanother iteration. This determination may be based on, for example, atimeout period expiring, a predetermined number of iterations beingreached, a loss (in embodiments that use a loss function) of aniteration being at or below a predetermined threshold, a performancemetric value of an iteration being calculated to be at or above apredetermined threshold (e.g., being at or above a performance metricvalue for another frequency in the channel or set of frequencies ofinterest), or a combination thereof. Other criteria may also be includedin the determination of whether to perform another iteration, asappropriate. If the computing device determines to perform anotheriteration, the method proceeds to operation 308. If the computing devicedetermines not to perform another iteration, the method proceeds tooperation 310.

At operation 308, the computing device updates the IQMC candidateparameter value. The computing device may update the IQMC candidateparameter value using the current IQMC candidate parameter value, usingthe performance metric value calculated in operation 304, using multipleprevious IQMC candidate parameter values, using a change in theperformance metric values calculated for the multiple previous IQMCcandidate parameter values, or in any other appropriate manner. Forexample, in some embodiments, a gradient ascent method is implemented,and as described below with respect to FIG. 4, one or more IQMCparameter values are changed in a direction of gradient. These updatedparameters are then used at operation 304 in a next iteration of theIQMC parameter optimization method 300.

At operation 310, one or more optimal, high performing, or bestperforming IQMC parameter values of a set of tested IQMC candidateparameter values (e.g., a set of IQMC candidate parameter values used inat least one iteration of the IQMC parameter optimization method 300)are identified, determined, or selected, and may be implemented in anIQMC (e.g., the IQMC may be adjusted to implement the optimal IQMCparameter values). Thus, an IQMC is implemented using optimized IQMCparameters that increase or maximize an IRR of the TX or RX chain. Suchan improved IRR can improve the performance of a device thatincorporates or implements the TX or RX chain.

Referring now to FIG. 4, FIG. 4 shows an example of a gradient ascent(or gradient descent) approach to iteratively determining IQMCparameters that maximize or increase a lowest IRR of a set of IRRs thatcorrespond to a set of frequencies of interest in a TX or RX chain.Although the depicted example method uses IRR as the IQMC performancemetric, a similar method can be implemented using SINR and/or SImR.

As a general overview, the example IQMC parameter optimization method400 shown in FIG. 4 includes choosing an initial solution (402), anddetermining whether additional iterations are appropriate (404). Ifadditional iterations are determined to be appropriate at operation(404), the example IQMC parameter optimization method 400 proceeds toselecting a frequency corresponding to a minimum IRR of a set of IRRs ofinterest (406), determining a gradient (408), updating IQMC parametersusing the gradient (410), increasing an iteration index or count (412),and returning to operation 404. If additional iterations are determinedto be inappropriate at operation 404, the example IQMC parameteroptimization method 400 proceeds to identifying an iteration index witha largest minimum IRR (414), and setting IQMC parameters that correspondto the iteration index identified in operation 414 (416). The processthen ends (418).

The following provides some example mathematical framework andbackground that can be used in implementing the IQMC parameteroptimization method 400. The following provides, among other things,tools for defining a performance metric in terms of one or more IQMCparameters, and a gradient of the performance metric with respect to theone or more IQMC parameters, which can be used in implementing the IQMCparameter optimization method 400.

Let x denote the vector of coefficients of IQMC block in TX or RX:

for RUC: x=[α_(TX/RX) ,d _(TX/RX,0) , . . . ,d _(TX/RX,L-1)]^(T)  (25);and a

for CVC: x=[Re{w _(TX/RX,0)}, . . . ,Re{w _(TX/RX,L-1)},Im{w_(TX/RX,0)}, . . . ,Im{w _(TX/RX,L-1)}]^(T)  (26).

In order to maximize IRR, one can formulate the problem as follows:

${\max\limits_{x}\mspace{14mu}{\mathcal{L}(x)}},$

where the cost function

(x) depends on IQMC parameters x. For IRR maximization, cost function

is a function of IRR_(TX/RX)(f) at the selected/measured continuous-timefrequencies f=f₁, . . . , f_(K) over the desired frequency band. ForSINR maximization, cost function

is a function of SINR_(TX/RX)(f) at the selected/measuredcontinuous-time frequencies f=f₁, . . . , f_(K) over the desiredfrequency band.

The optimization problem above can be solved, for example, using agradient ascent (GA) search method (although other approaches can alsobe used) whose update rule at the (

+1) iteration is given by:

₊₁ =x _(l)+μ∀_(x)

(x)|_(x=x) _(l) ,  (27)

where μ in Equation 27 denotes the step size, which can be a function ofiteration index, and for a CVC:

$\begin{matrix}{{{\nabla_{x}{\mathcal{L}(x)}} = {\left\lbrack {\frac{\partial{\mathcal{L}(x)}}{{\partial{Re}}\left\{ x_{0} \right\}},\ldots\mspace{14mu},\frac{\partial{\mathcal{L}(x)}}{{\partial{Re}}\left\{ x_{L - 1} \right\}},\frac{\partial{\mathcal{L}(x)}}{{\partial{Im}}\left\{ x_{0} \right\}},\ldots\mspace{14mu},\frac{\partial{\mathcal{L}(x)}}{{\partial{Im}}\left\{ x_{L - 1} \right\}}} \right\rbrack^{T} = \left\lbrack {\frac{\partial{\mathcal{L}(x)}}{{\partial{Re}}\left\{ w_{{{TX}\text{/}{RX}},0} \right\}},\ldots\mspace{14mu},\frac{\partial{\mathcal{L}(x)}}{{\partial{Re}}\left\{ w_{{{TX}\text{/}{RX}},{L - 1}} \right\}},\frac{\partial{\mathcal{L}(x)}}{{\partial{Im}}\left\{ w_{{{TX}\text{/}{RX}},0} \right\}},\ldots\mspace{14mu},\frac{\partial{\mathcal{L}(x)}}{{\partial{Im}}\left\{ w_{{{TX}\text{/}{RX}},{L - 1}} \right\}}} \right\rbrack^{T}}},} & (28)\end{matrix}$

and

for an RVC:

$\begin{matrix}{{\nabla_{x}{\mathcal{L}(x)}} = {\left\lbrack {\frac{\partial{\mathcal{L}(x)}}{\partial x_{0}},\ldots\mspace{14mu},\ \frac{\partial{\mathcal{L}(x)}}{\partial x_{L}}} \right\rbrack^{T} = {\left\lbrack {\frac{\partial{\mathcal{L}(x)}}{\partial\alpha_{{TX}/{RX}}},\frac{\partial{\mathcal{L}(x)}}{\partial d_{{{TX}/{RX}},0}},\ldots\mspace{14mu},\ \frac{\partial{\mathcal{L}(x)}}{\partial d_{{{TX}/{RX}},{L - 1}}}} \right\rbrack^{T}.}}} & (29)\end{matrix}$

One can select a starting point x₀ as appropriate (e.g., one can useno-IQMC solutions). The iterations are continued until one reachesconvergence or the maximum number of allowed iterations. One then setsIQMC coefficients to the ones that provide the optimized (largest, inthis case) cost function over the iterations.

Let

denote the set of desired continuous-time frequencies over the desiredfrequency band:

={f₁, . . . , f_(K)}. Some example cost functions for IRR maximizationare:

$\begin{matrix}{\mathcal{L} = {{IRR}(f)}} & (30)\end{matrix}$

minimum IRR over desired frequencies with

∇_(x)ℒ(x) = ∇_(x)IRR(f);

$\begin{matrix}{\mathcal{L} = {\frac{1}{\mathcal{S}}{\sum_{f \in}{{IRR}(f)}}}} & (31)\end{matrix}$

mean of IRR over desired frequencies with

${\nabla_{x}{\mathcal{L}(x)}} = {\frac{1}{}{\sum_{f \in}{{\nabla_{x}{{IRR}(f)}}\text{;}}}}$

$\begin{matrix}{\mathcal{L} = \left( {\prod_{f \in}{{IRR}(f)}} \right)^{\frac{1}{}}} & (32)\end{matrix}$

geometric mean of IRR over desired frequencies (note that gradientcalculation for geometric mean can be simpler if one converts IRR tologarithmic scale and uses regular mean instead as the maximization inlog domain is equivalent to the maximization in linear scale:

∇ x ⁢ ln ⁢ ⁢ ℒ ⁡ ( x ) = 1   ⁢ ∑ f ∈ ⁢ ∇ x ⁢ IRR ⁡ ( f ) IRR ⁡ ( f ) ⁢ ; ⁢ ⁢ and ⁢ ⁢ℒ = min k ⁢ ( ∏ f ∈ ⁢ k ⁢ IRR ⁡ ( f ) ) 1  ⁢ k  ( 33 )

minimum geometric mean of IRR per CC in carrier aggregation scenarios,where

_(k) is the set of selected frequencies that lie in the frequency rangeof the kth CC.

Some example cost functions for SINR maximization are:

$\begin{matrix}{\mathcal{L} = {{SINR}(f)}} & (34)\end{matrix}$

minimum SINR over desired frequencies with

∇_(x)ℒ(x) = ∇_(x)SINR(f);

and

=

log₂(1+SINR(f))  (35):

sum throughput over desired frequencies with

${\nabla_{x}{\mathcal{L}(x)}} = {\frac{1}{\ln\mspace{14mu} 2}{\sum_{f \in}{\frac{\nabla_{x}{{SINR}(f)}}{1 + {{SINR}(f)}}.}}}$

In order to use Equations 30 through Equations 35 and calculate agradient of

(x), one can assume that one already has estimates of G_(1TX)(f),G_(2TX)(f) or G_(1RX)(f), G_(2RX)(f) at frequencies f₁, . . . , f_(K)and use them to optimize the IQMC coefficients.

A gradient of IRR with respect to IQMC coefficients can be computed as

$\begin{matrix}{{{\nabla_{x}{{IRR}_{{TX}\text{/}{RX}}(f)}} = \frac{{{{J_{2}(f)}}^{2}{\nabla_{x}\left( {{J_{1}(f)}}^{2} \right)}} - {{{J_{1}(f)}}^{2}{\nabla_{x}\left( {{J_{2}(f)}}^{2} \right)}}}{{{J_{2}(f)}}^{4}}},} & (36)\end{matrix}$

where

$\begin{matrix}{\mspace{79mu}{{J_{1}(f)} = \left\{ {\begin{matrix}{{{F_{1{TX}}(f)}{G_{1{TX}}(f)}} + {{F_{2{TX}}^{*}\left( {- f} \right)}{G_{2{TX}}(f)}}} & {{for}\mspace{14mu}{TX}} \\{{{F_{1{RX}}(f)}{G_{1{RX}}(f)}} + {{F_{2{RX}}(f)}{G_{2{RX}}^{*}\left( {- f} \right)}}} & {{for}\mspace{14mu}{RX}}\end{matrix}\text{;}} \right.}} & (37) \\{{J_{2}(f)} = \left\{ {\begin{matrix}{{{F_{2{TX}}(f)}{G_{1{TX}}(f)}} + {{F_{1{TX}}^{*}\left( {- f} \right)}{G_{2{TX}}(f)}}} & {{for}\mspace{14mu}{TX}} \\{{{F_{1{RX}}(f)}{G_{2{RX}}(f)}} + {{F_{2{RX}}(f)}{G_{1{RX}}^{*}\left( {- f} \right)}}} & {{for}\mspace{14mu}{RX}}\end{matrix}\text{;}\mspace{14mu}{and}} \right.} & (38) \\{{\nabla_{x}\left( {{J_{1}(f)}}^{2} \right)} = \left\{ {\begin{matrix}{2\;{Re}\left\{ \left( {{{G_{1{TX}}(f)} \times {\nabla_{x}{F_{1{TX}}(f)}}} +} \right. \right.} & {{for}\mspace{14mu}{TX}} \\\left. {\left. {{G_{2{TX}}(f)} \times {\nabla_{x}{F_{2{TX}}^{*}\left( {- f} \right)}}} \right){J_{1}^{*}(f)}} \right\} & \; \\{2\;{Re}\left\{ \left( {{{G_{1{RX}}(f)} \times {\nabla_{x}{F_{1{RX}}(f)}}} +} \right. \right.} & {{for}\mspace{14mu}{RX}} \\\left. {\left. {{G_{2{RX}}^{*}\left( {- f} \right)} \times {\nabla_{x}{F_{2{RX}}(f)}}} \right){J_{1}^{*}(f)}} \right\} & \;\end{matrix}\text{;}} \right.} & (39) \\{{\nabla_{x}\left( {{J_{2}(f)}}^{2} \right)} = \left\{ {\begin{matrix}{2\;{Re}\left\{ \left( {{{G_{1{TX}}(f)} \times {\nabla_{x}{F_{2{TX}}(f)}}} +} \right. \right.} & {{for}\mspace{14mu}{TX}} \\\left. {\left. {{G_{2{TX}}(f)} \times {\nabla_{x}{F_{1{TX}}^{*}\left( {- f} \right)}}} \right){J_{2}^{*}(f)}} \right\} & \; \\{2\;{Re}\left\{ \left( {{{G_{2{RX}}(f)} \times {\nabla_{x}{F_{1{RX}}(f)}}} +} \right. \right.} & {{for}\mspace{14mu}{RX}} \\\left. {\left. {{G_{1{RX}}^{*}\left( {- f} \right)} \times {\nabla_{x}{F_{2{RX}}(f)}}} \right){J_{2}^{*}(f)}} \right\} & \;\end{matrix}.} \right.} & (40)\end{matrix}$

Similarly, a gradient of SINR with respect to IQMC coefficients can becomputed as:

$\begin{matrix}{{\nabla_{x}{{SINR}_{TX}(f)}} = {\frac{{\left( {{{J_{2}(f)}}^{2} + \sigma_{TX}^{2}} \right){\nabla_{x}\left( {{J_{1}(f)}}^{2} \right)}} - {{{J_{1}(f)}}^{2}{\nabla_{x}\left( {{J_{2}(f)}}^{2} \right)}}}{\left( {{{J_{2}(f)}}^{2} + \sigma_{TX}^{2}} \right)^{2}}\text{;}}} & (41) \\{\mspace{79mu}{{\nabla_{x}{{SINR}_{RX}(f)}} = {\frac{\begin{matrix}{{\left( {{{J_{2}(f)}}^{2} + {\sigma_{RX}^{2}{J_{3}(f)}}} \right){\nabla_{x}\left( {{J_{1}(f)}}^{2} \right)}} -} \\{{{J_{1}(f)}}^{2}\left( {{\nabla_{x}\left( {{J_{2}(f)}}^{2} \right)} + {\sigma_{RX}^{2}{\nabla_{x}{J_{3}(f)}}}} \right)}\end{matrix}}{\left( {{{J_{2}(f)}}^{2} + {\sigma_{RX}^{2}{J_{3}(f)}}} \right)^{2}}\text{;}}}} & (42)\end{matrix}$

where (f), J₁(f), ∀_(x)(|J₁(f)|²), and ═_(x)(|J₂(f)|²) are defined inEqs. 37-40, and J₃(f) and its gradient are defined as:

J ₃(f)=|F _(1RX)(f)|² +|F _(2RX)(f)|²  (43)

∀_(x) J ₃(f)=2 Re{F* _(1RX)(f)∀_(x) F _(1RX)(f)+F* _(2RX)(f)∀_(x) F_(2RX)(f)}  (44).

Define q_(k) ^(T)=[1, e^(−j2πf) ^(k) ^(/F) ^(s) , . . . , e^(−j2πf) ^(k)^((L-1)/F) ^(s) ] as the DFT vector at continuous-time frequency f_(k),where F_(s) denotes sampling rate on which the IQMC operates. Supposeone uses FIR filters of length L IQMCs: w_(1TX)[n]=Σ_(i=0)^(L-1)w_(1TX,i)δ[n−i], w_(2TX)[n]=Σ_(i=0) ^(L-1)w_(2TX,i)δ[n−i],d_(TX)[n]=Σ_(i=0) ^(L-1)d_(TX,i)δ[n−i], w_(RX)[n]=Σ_(i=0)^(L-1)w_(RX,i)δ[n−i], and d_(RX)[n]=Σ_(i=0) ^(L-1)d_(RX,i)δ[n−i] (wherethese five filter parameters respectively correspond to the IQMCs shownin FIGS. 2A-2E). Then, ∀_(x)F_(1TX)(f), ∀_(x)F_(2TX)(f),∀_(x)F_(1RX)(f), ∀_(x)F_(2RX)(f) for the IQMCs shown in FIGS. 2A-2E canbe computed as:

$\begin{matrix}{\mspace{79mu}{{{For}\mspace{14mu}{IQMC}\mspace{14mu} 202\mspace{14mu}{with}}\mspace{11mu}\;{x = {\left\lbrack {{{Re}\left\{ w_{{1{TX}},0} \right\}},\ldots\mspace{14mu},{{Re}\left\{ w_{{1{TX}},{L - 1}} \right\}},{{Im}\left\{ w_{{1{TX}},0} \right\}},\ldots\mspace{14mu},\ {{Im}\left\{ w_{{1{TX}},{L - 1}} \right\}}} \right\rbrack^{T}\text{:}}}}} & \; \\{\mspace{79mu}{{\frac{\partial{F_{1{TX}}\left( f_{k} \right)}}{\partial x_{i}} = {{\frac{\partial}{\partial x_{i}}\left( e^{{- j}2\pi\frac{f_{k}}{F_{s}}T_{D}} \right)} = 0}},{i = 0},\ldots\mspace{14mu},{{2L} - 1}}} & (45) \\{\frac{\partial{F_{2{TX}}\left( f_{k} \right)}}{\partial x_{i}} = {\frac{{\partial q_{k}^{T}}w_{1,{TX}}}{\partial x_{i}} = \left\{ {\begin{matrix}e^{{- j}\; 2\;\pi\;{{if}_{k}/F_{s}}} & {{i = 0},\ldots\mspace{14mu},{L - 1}} \\{je}^{{- j}\; 2\;{\pi{({i - L})}}{f_{k}/F_{s}}} & {{i = L},\ldots\mspace{14mu},{{2L} - 1}}\end{matrix},} \right.}} & (46)\end{matrix}$

where w_(1,TX)=[w_(1TX,0), . . . , w_(1TX,L-1)]^(T).

$\begin{matrix}{\mspace{79mu}{{For}\mspace{14mu}{IQMC}\mspace{14mu} 203\mspace{14mu}{with}}\mspace{14mu}} & \; \\{x = {\left\lbrack {{{Re}\left\{ w_{{2{TX}},0} \right\}},\ldots\mspace{14mu},{{Re}\left\{ w_{{2TX},{L - 1}} \right\}},{{Im}\left\{ w_{{2{TX}},0} \right\}},\ldots\mspace{14mu},{{Im}\left\{ w_{{2{TX}},{L - 1}} \right\}}} \right\rbrack^{T}\text{:}}} & \; \\{\frac{\partial{F_{1{TX}}\left( f_{k} \right)}}{\partial x_{i}} = {{\frac{\partial}{\partial x_{i}}\left( {e^{{- j}2\pi\frac{f_{k}}{F_{s}}T_{D}} + \frac{q_{k}^{T}w_{2,{TX}}}{2}} \right)} = \left\{ \begin{matrix}\frac{e^{{- j}\; 2\pi\;{{if}_{k}/F_{s}}}}{2} & {{i = 0},\ldots\mspace{14mu},{L - 1}} \\\frac{e^{{- j}\; 2{\pi(\;{i - L})}{f_{k}/F_{s}}}}{2} & {{i = L},\ldots\mspace{14mu},{{2L} - 1}}\end{matrix} \right.}} & (47) \\{\frac{\partial{F_{2{TX}}\left( f_{k} \right)}}{\partial x_{i}} = {{\frac{\partial}{\partial x_{i}}\left( \frac{q_{k}^{T}w_{2,{TX}}^{*}}{2} \right)} = \left\{ {\begin{matrix}\frac{e^{{- j}\; 2\pi\;{{if}_{k}/F_{s}}}}{2} & {{i = 0},\ldots\mspace{14mu},{L - 1}} \\{- \frac{{je}^{{- j}\; 2{\pi(\;{i - L})}{f_{k}/F_{s}}}}{2}} & {{i = L},\ldots\mspace{14mu},{{2L} - 1}}\end{matrix},} \right.}} & (48)\end{matrix}$

where w_(2,TX)=[w_(2TX,0), . . . , w_(2TX,L-1)]^(T).

$\begin{matrix}{{{For}\mspace{14mu}{IQMC}\mspace{14mu} 204\mspace{14mu}{with}}{x = {\left\lbrack {\alpha,d_{TX}^{T}} \right\rbrack^{T} = {\left\lbrack {\alpha,d_{{TX},0},\ldots\mspace{14mu},d_{{TX},{L - 1}}} \right\rbrack^{T}\text{:}}}}} & \; \\{\frac{\partial{F_{1{TX}}\left( f_{k} \right)}}{\partial x_{i}} = \left\{ \begin{matrix}{\frac{j}{2}q_{k}^{T}d_{TX}} & {i = 0} \\{\frac{\left( {1 + {j\alpha_{TX}}} \right)}{2}e^{{- j}2{\pi{({i - 1})}}{f_{k}/F_{s}}}} & {\ {{i = 1},\ldots\mspace{14mu},L}}\end{matrix} \right.} & (49) \\{\frac{\partial{F_{2{TX}}\left( f_{k} \right)}}{\partial x_{i}} = \left\{ {\begin{matrix}{{\frac{j}{2}q_{k}^{T}d_{TX}}\ } & {i = 0} \\{{- \frac{\left( {1 - {j\alpha_{TX}}} \right)}{2}}e^{{- j}2{\pi{({i - 1})}}{f_{k}/F_{s}}}} & {\ {{i = 1},\ldots\mspace{14mu},L}}\end{matrix},} \right.} & (50)\end{matrix}$

where d_(TX)=[d_(TX,0), . . . , d_(TX,L-1)]^(T).

$\begin{matrix}{\mspace{79mu}{{For}\mspace{14mu}{IQMC}\mspace{14mu} 206\mspace{14mu}{with}}\mspace{14mu}} & \; \\{x = {\left\lbrack {{{Re}\left\{ w_{{RX},0} \right\}},\ldots\mspace{14mu},{{Re}\left\{ w_{{RX},{L - 1}} \right\}},{{Im}\left\{ w_{{RX},0} \right\}},\ldots\mspace{14mu},{{Im}\left\{ w_{{RX},{L - 1}} \right\}}} \right\rbrack^{T}\text{:}}} & \; \\{\frac{\partial{F_{1{RX}}\left( f_{k} \right)}}{\partial x_{i}} = {{\frac{\partial}{\partial x_{i}}\left( {e^{{- j}2\pi\frac{f_{k}}{F_{s}}T_{D}} + \frac{q_{k}^{T}w_{RX}}{2}} \right)} = \left\{ \begin{matrix}\frac{e^{{- j}\; 2\pi\;{{if}_{k}/F_{s}}}}{2} & {{i = 0},\ldots\mspace{14mu},{L - 1}} \\\frac{{je}^{{- j}\; 2{\pi(\;{i - L})}{f_{k}/F_{s}}}}{2} & {{i = L},\ldots\mspace{14mu},{{2L} - 1}}\end{matrix} \right.}} & (51) \\{\frac{\partial{F_{2{RX}}\left( f_{k} \right)}}{\partial x_{i}} = {{\frac{\partial}{\partial x_{i}}\left( \frac{q_{k}^{T}w_{RX}}{2} \right)} = \left\{ {\begin{matrix}\frac{e^{{- j}\; 2\pi\;{{if}_{k}/F_{s}}}}{2} & {{i = 0},\ldots\mspace{14mu},{L - 1}} \\\frac{{je}^{{- j}\; 2{\pi(\;{i - L})}{f_{k}/F_{s}}}}{2} & {{i = L},\ldots\mspace{14mu},{{2L} - 1}}\end{matrix},} \right.}} & (52)\end{matrix}$

where w_(RX)=[w_(RX,0), . . . , w_(RX,L-1)]^(T).

$\begin{matrix}{{{For}\mspace{14mu}{IQMC}\mspace{14mu} 208\mspace{11mu}{with}}{x = {\left\lbrack {\alpha_{RX},d_{RX}^{T}} \right\rbrack^{T} = {\left\lbrack {\alpha_{RX},\ d_{{RX},0},\ldots\mspace{14mu},d_{{RX},{L - 1}}} \right\rbrack^{T}\text{:}}}}} & \; \\{\frac{\partial{F_{1{RX}}\left( f_{k} \right)}}{\partial x_{i}} = \left\{ \begin{matrix}{\frac{j}{2}e^{{- j}\; 2\pi\frac{f_{k}}{F_{s}}T_{D}}} & {i = 0} \\{\frac{1}{2}e^{{- j}\; 2{\pi(\;{i - 1})}{f_{k}/F_{s}}}} & {{i = 1},\ldots\mspace{14mu},L}\end{matrix} \right.} & (53) \\{\frac{\partial{F_{2{RX}}\left( f_{k} \right)}}{\partial x_{i}} = \left\{ {\begin{matrix}{\frac{j}{2}e^{{- j}\; 2\pi\frac{f_{k}}{F_{s}}T_{D}}} & {i = 0} \\{{- \frac{1}{2}}e^{{- j}\; 2{\pi(\;{i - 1})}{f_{k}/F_{s}}}} & {{i = 1},\ldots\mspace{14mu},L}\end{matrix},} \right.} & (54)\end{matrix}$

where d_(RX)=[d_(RX,0), . . . , d_(RX,L-1)]^(T).

The equations immediately above provide some examples of mathematicalmodeling that can be appropriate when G_(1TX)(f), G_(2TX)(f) orG_(1RX)(f), G_(2RX)(f) at frequencies f₁, . . . , f_(K) are known (e.g.,are estimated by sending pilot signals at continuous time basebandfrequencies f₁, . . . , f_(K)).

Equations 13, 15, 16, 22, and 24 provide some examples of mathematicalmodeling of IRR that can be used when G_(1TX)(f), G_(2TX)(f) orG_(1RX)(f), G_(2RX)(f) are not known separately and instead V_(TX)(f)and ϕ_(TX) or V_(RX)(f) and ϕ_(RX) are known (e.g., are estimated bysending pilot signals at continuous time baseband frequencies f₁, . . ., f_(K)). The following provides some examples of mathematical modelingof gradients of cost functions that correspond to such IRR modelling.

To compute a gradient of such a cost function, ∀_(x)

(x), for

${\mathcal{L} = {\min\limits_{f \in \mathcal{S}}{{IR}{R(f)}}}},$

one can let

$i_{\ell} = {\underset{f \in \mathcal{S}}{\arg\min}{{IRR}(f)}}$

denote the index of the frequency that has the smallest IRR value at the

th iteration. Define q_(k) ^(T)=[1, e^(−j2πf) ^(k) ^(/F) ^(s) , . . . ,e^(−j2πf) ^(k) ^((L-1)/F) ^(s) ] as the DFT vector at continuous-timefrequency f_(k), where F_(s) denotes a sampling rate on which IQMCoperates. Then, a gradient of the cost function

$\mathcal{L} = {\min\limits_{f \in \mathcal{S}}{{IR}{R(f)}}}$

with respect to RVC coefficients can be given by:

For TX RVCs:

$\begin{matrix}{{{{\nabla_{x}{\min\limits_{f \in \mathcal{S}}{{IR}{R(f)}}}} = \frac{{{{J_{2}\left( f_{i_{\ell}} \right)}}^{2}{\nabla_{x}\left( {{J_{1}\left( f_{i_{\ell}} \right)}}^{2} \right)}} - {{{J_{1}\left( f_{i_{\ell}} \right)}}^{2}{\nabla_{x}\left( {{J_{2}\left( f_{i_{\ell}} \right)}}^{2} \right)}}}{{{J_{2}\left( f_{i_{\ell}} \right)}}^{4}}},\mspace{20mu}{where}}\mspace{20mu}{{{J_{1}\left( f_{i_{\ell}} \right)} = {{{V_{TX}\left( f_{i_{\ell}} \right)}e^{{- j}\;\phi_{TX}}} + {e^{{+ j}2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}q_{i_{\ell}}^{T}{d_{TX}\left( {{j\alpha_{TX}} + 1} \right)}}}},}} & (55) \\{\mspace{79mu}{{{J_{2}\left( f_{i_{\ell}} \right)} = {{{V_{TX}\left( f_{i_{\ell}} \right)}e^{{- j}\;\phi_{TX}}} + {e^{{+ j}2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}q_{i_{\ell}}^{T}{d_{TX}\left( {{j\alpha_{TX}} - 1} \right)}}}},{and}}} & (56) \\{\mspace{79mu}{{{\nabla_{x}\left( {{J_{1}\left( f_{i_{\ell}} \right)}}^{2} \right)} = {2\begin{bmatrix}{{{Im}\left\{ {{J_{1}\left( f_{i_{\ell}} \right)}q_{i_{\ell}}^{H}d_{TX}e^{{- j}2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}} \right\}},} \\{{Re}\left\{ {{J_{1}\left( f_{i_{\ell}} \right)}{q_{i_{\ell}}^{H}\left( {{{- j}\alpha_{TX}} + 1} \right)}e^{{- j}2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}} \right\}}\end{bmatrix}}^{T}},\mspace{20mu}{{\nabla_{x}\left( {{J_{2}\left( f_{i_{\ell}} \right)}}^{2} \right)} = {{2\begin{bmatrix}{{{Im}\left\{ {{J_{2}\left( f_{i_{\ell}} \right)}q_{i_{\ell}}^{H}d_{TX}e^{{- j}2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}} \right\}},} \\{{Re}\left\{ {{J_{2}\left( f_{i_{\ell}} \right)}{q_{i_{\ell}}^{H}\left( {{j\alpha_{TX}} + 1} \right)}e^{{- j}2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}} \right\}}\end{bmatrix}}^{T}.}}}} & (57)\end{matrix}$

For RX RVCs:

$\begin{matrix}{{{{\nabla_{x}{\min\limits_{f \in \mathcal{S}}{{IR}{R(f)}}}} = \frac{{{{J_{2}\left( f_{i_{\ell}} \right)}}^{2}{\nabla_{x}\left( {{J_{1}\left( f_{i_{\ell}} \right)}}^{2} \right)}} - {{{J_{1}\left( f_{i_{\ell}} \right)}}^{2}{\nabla_{x}\left( {{J_{2}\left( f_{i_{\ell}} \right)}}^{2} \right)}}}{{{J_{2}\left( f_{i_{\ell}} \right)}}^{4}}},\mspace{20mu}{where}}\mspace{20mu}{{{J_{1}\left( f_{i_{\ell}} \right)} = {{{V_{RX}\left( f_{i_{\ell}} \right)}e^{{+ j}\;\phi_{RX}}} + {e^{{+ j}\; 2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}\frac{q_{i_{\ell}^{T}}d_{RX}}{{j\;\alpha_{RX}} + 1}}}},}} & (58) \\{\mspace{79mu}{{{J_{2}\left( f_{i_{\ell}} \right)} = {{{V_{RX}^{*}\left( {- f_{i_{\ell}}} \right)}e^{{- j}\;\phi_{RX}}} + {e^{{+ j}\; 2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}\frac{q_{i_{\ell}^{T}}d_{RX}}{{j\;\alpha_{RX}} + 1}}}},{and}}} & (57) \\{{{\nabla_{x}\left( {{J_{1}\left( f_{i_{\ell}} \right)}}^{2} \right)} = {2\left\lbrack {{{- {Im}}\left\{ {\frac{{J_{1}\left( f_{i_{\ell}} \right)}q_{i_{\ell}}^{H}d_{RX}}{\left( {{{- j}\alpha_{RX}} + 1} \right)^{2}}e^{{- j}2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}} \right\}},{{Re}\left\{ {\frac{{J_{1}\left( f_{i_{\ell}} \right)}q_{i_{\ell}}^{H}}{{{- j}\alpha_{RX}} + 1}e^{{- j}2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}} \right\}}} \right\rbrack}^{T}},{{\nabla_{x}\left( {{J_{2}\left( f_{i_{\ell}} \right)}}^{2} \right)} = {{2\left\lbrack {{{Im}\left\{ {\frac{{J_{2}\left( f_{i_{\ell}} \right)}q_{i_{\ell}}^{H}d_{RX}}{\left( {{{- j}\alpha_{RX}} + 1} \right)^{2}}e^{{- j}2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}} \right\}},{{- {Re}}\left\{ {\frac{{J_{2}\left( f_{i_{\ell}} \right)}q_{i_{\ell}}^{H}}{{{- j}\alpha_{RX}} + 1}e^{{- j}2\pi\frac{f_{i_{\ell}}}{F_{s}}T_{D}}} \right\}}} \right\rbrack}^{T}.}}} & (59)\end{matrix}$

One can follow similar steps and obtain the gradient for CVCs or otherIQMC structures for other cost functions. The cost functions can beselected as appropriate to optimize a desired performance metric. Forexample, for SImR, using the formula shown in Equation 14 or in Equation23, one can define any of the following example cost functions:

$\mathcal{L} = {\min\limits_{f \in \mathcal{S}}{{SIm}{R(f)}\text{:}}}$

minimum SImR over desired frequencies (frequencies of interest);

$\mathcal{L} = {\frac{1}{\mathcal{S}}\Sigma_{f \in \mathcal{S}}{SIm}{R(f)}\text{:}}$

mean of SImR over desired frequencies;

$\mathcal{L} = {\left( {\Pi_{f \in \mathcal{S}}{SIm}{R(f)}} \right)^{\frac{1}{\mathcal{S}}}\text{:}}$

geometric mean of SImR over desired frequencies; and

$\mathcal{L} = {\min\limits_{k}{\left( {\prod_{f \in {\mathcal{C}\mathcal{C}}_{k}}{{SImR}(f)}} \right)^{\frac{1}{|{\mathcal{C}\mathcal{C}}_{k}|}}\text{:}}}$

minimum geometric mean of SImR per component carrier (CC) in carrieraggregation (CA) scenarios.

With these example cost functions, one can compute a gradient for theSImR of choice using a similar framework as set forth above for IRR.Additionally, a particular IRR or SINR to optimize (that is, an IRR oran SINR corresponding to a particular frequency) may be made selectedfrom a similar list as above, e.g., may be a minimum IRR or SINR overdesired frequencies, may be a mean IRR or SINR over desired frequencies,may be a geometric mean IRR or SINR over desired frequencies, or may bea minimum geometric mean IRR or SINR per CC in CA scenarios.Optimization of other IRRs, SINRs, or SImRs (that is, IRRs, SINRs, orSImRs corresponding to different frequencies) may also be implemented,as appropriate, using techniques described herein.

FIG. 5 shows a diagram of an electronic device 501 in a networkenvironment 500, according to some embodiments. Referring to FIG. 5, theelectronic device 501 in the network environment 500 may communicatewith an electronic device 502 via a first network 598 (e.g., ashort-range wireless communication network, such as a Wi-Fi network), oran electronic device 504 or a server 508 via a second network 599 (e.g.,a long-range wireless communication network). The electronic device 501may communicate with the electronic device 504 via the server 508. Theelectronic device 501 may include a processor 520, a memory 530, aninput device 550, a sound output device 555, a display device 560, anaudio module 570, a sensor module 576, an interface 577, a haptic module579, a camera module 580, a power management module 588, a battery 589,a communication module 590, a subscriber identification module (SIM)596, and/or an antenna module 597. In one embodiment, at least one ofthe components (e.g., the display device 560 or the camera module 580)may be omitted from the electronic device 501, or one or more othercomponents may be added to the electronic device 501. In one embodiment,some of the components may be implemented as a single integrated circuit(IC). For example, the sensor module 576 (e.g., a fingerprint sensor, aniris sensor, or an illuminance sensor) may be embedded in the displaydevice 560 (e.g., a display), or the display device 560 may include oneor more sensors in addition to the sensor module 576.

In some embodiments, the device 501 may include the TX 100 or the RX 110(or both). The device 501 may include an IQMC, including at least one ofthe IQMCs shown in FIGS. 2A-2E. In some embodiments, the device 501 mayinclude a computing device configured to implement an IQMC optimizationtechnique, such as the IQMC parameter optimization method 300 shown inFIG. 3, or the IQMC parameter optimization method 400 shown in FIG. 4.

The processor 520 may execute, for example, software (e.g., a program540) to control at least one other component (e.g., a hardware or asoftware component) of the electronic device 501 coupled with theprocessor 520, and may perform various data processing and/orcomputations. As at least a part of the data processing and/orcomputations, the processor 520 may load a command or data received fromanother component (e.g., the sensor module 576 or the communicationmodule 590) in volatile memory 532, process the command or the datastored in the volatile memory 532, and store resulting data innon-volatile memory 534. The processor 520 may include a main processor521 (e.g., a central processing unit (CPU) or an application processor(AP)), and an auxiliary processor 523 (e.g., a graphics processing unit(GPU), an image signal processor (ISP), a sensor hub processor, or acommunication processor (CP)) that is operable independently from, or inconjunction with, the main processor 521. Additionally or alternatively,the auxiliary processor 523 may be adapted to consume less power thanthe main processor 521, and/or execute a particular function. Theauxiliary processor 523 may be implemented as being separate from, or asa part of, the main processor 521.

The auxiliary processor 523 may control at least some of the functionsor states related to at least one component (e.g., the display device560, the sensor module 576, or the communication module 590) from amongthe components of the electronic device 501, instead of the mainprocessor 521 while the main processor 521 is in an inactive (e.g.,sleep) state, or together with the main processor 521 while the mainprocessor 521 is in an active state (e.g., executing an application).According to one embodiment, the auxiliary processor 523 (e.g., an imagesignal processor or a communication processor) may be implemented as apart of another component (e.g., the camera module 580 or thecommunication module 590) functionally related to the auxiliaryprocessor 523.

The memory 530 may store various data used by at least one component(e.g., the processor 520 or the sensor module 576) of the electronicdevice 501. The various data may include, for example, software (e.g.,the program 540) and input data or output data for a command relatedthereto. The memory 530 may include the volatile memory 532 and/or thenon-volatile memory 534.

The program 540 may be stored in the memory 530 as software, and mayinclude, for example, an operating system (OS) 542, middleware 544, oran application 546.

The input device 550 may receive a command or data to be used by anothercomponent (e.g., the processor 520) of the electronic device 501, fromthe outside (e.g., a user) of the electronic device 501. The inputdevice 550 may include, for example, a microphone, a mouse, and/or akeyboard.

The sound output device 555 may output sound signals to the outside ofthe electronic device 501. The sound output device 555 may include, forexample, a speaker or a receiver. The speaker may be used for generalpurposes, such as playing multimedia or recording, and the receiver maybe used for receiving an incoming call. According to one embodiment, thereceiver may be implemented as being separate from, or as a part of, thespeaker.

The display device 560 may visually provide information to the outside(e.g., a user) of the electronic device 501. The display device 560 mayinclude, for example, a display, a hologram device, and/or a projectorand control circuitry to control a corresponding one of the display, thehologram device, and the projector. According to one embodiment, thedisplay device 560 may include touch circuitry adapted to detect atouch, or sensor circuitry (e.g., a pressure sensor) adapted to measurethe intensity of force incurred by the touch.

The audio module 570 may convert a sound into an electrical signal andvice versa. According to one embodiment, the audio module 570 may obtainthe sound via the input device 550, and/or output the sound via thesound output device 555 or a headphone of an external electronic device502 directly (e.g., wired) or wirelessly coupled with the electronicdevice 501.

The sensor module 576 may detect an operational state (e.g., power ortemperature) of the electronic device 501 and/or an environmental state(e.g., a state of a user) external to the electronic device 501, andthen generate an electrical signal or data value corresponding to thedetected state. The sensor module 576 may include, for example, agesture sensor, a gyro sensor, an atmospheric pressure sensor, amagnetic sensor, an acceleration sensor, a grip sensor, a proximitysensor, a color sensor, an infrared (IR) sensor, a biometric sensor, atemperature sensor, a humidity sensor, and/or an illuminance sensor.

The interface 577 may support one or more specified protocols to be usedfor the electronic device 501 to be coupled with the external electronicdevice 502 directly (e.g., wired) or wirelessly. According to oneembodiment, the interface 577 may include, for example, a highdefinition multimedia interface (HDMI), a universal serial bus (USB)interface, a secure digital (SD) card interface, and/or an audiointerface.

A connecting terminal 578 may include a connector via which theelectronic device 501 may be physically connected with the externalelectronic device 502. According to one embodiment, the connectingterminal 578 may include, for example, an HDMI connector, a USBconnector, an SD card connector, and/or an audio connector (e.g., aheadphone connector).

The haptic module 579 may convert an electrical signal into a mechanicalstimulus (e.g., a vibration or a movement) and/or an electrical stimuluswhich may be recognized by a user via tactile sensation or kinestheticsensation. According to one embodiment, the haptic module 579 mayinclude, for example, a motor, a piezoelectric element, and/or anelectrical stimulator.

The camera module 580 may capture a still image or moving images.According to one embodiment, the camera module 580 may include one ormore lenses, image sensors, image signal processors, and/or flashes.

The power management module 588 may manage power supplied to theelectronic device 501. The power management module 588 may beimplemented as at least a part of, for example, a power managementintegrated circuit (PMIC).

The battery 589 may supply power to at least one component of theelectronic device 501. According to one embodiment, the battery 589 mayinclude, for example, a primary cell which is not rechargeable, asecondary cell which is rechargeable, and/or a fuel cell.

The communication module 590 may support establishing a direct (e.g.,wired) communication channel or a wireless communication channel betweenthe electronic device 501 and the external electronic device (e.g., theelectronic device 502, the electronic device 504, and/or the server 508)and performing communication via the established communication channel.The communication module 590 may include one or more communicationprocessors that are operable independently from the processor 520 (e.g.,the AP) and may support a direct (e.g., wired) communication and/or awireless communication. According to one embodiment, the communicationmodule 590 may include a wireless communication module 592 (e.g., acellular communication module, a short-range wireless communicationmodule, and/or a global navigation satellite system (GNSS) communicationmodule) or a wired communication module 594 (e.g., a local area network(LAN) communication module or a power line communication (PLC) module).A corresponding one of these communication modules may communicate withthe external electronic device via the first network 598 (e.g., ashort-range communication network, such as Bluetooth®, wireless-fidelity(Wi-Fi) direct, and/or a standard of the Infrared Data Association(IrDA)) or the second network 599 (e.g., a long-range communicationnetwork, such as a cellular network, the Internet, and/or a computernetwork (e.g., LAN or wide area network (WAN)). Bluetooth® is aregistered trademark of Bluetooth SIG, Inc., Kirkland, Wash. Thesevarious types of communication modules may be implemented as a singlecomponent (e.g., a single IC), or may be implemented as multiplecomponents (e.g., multiple ICs) that are separate from each other. Thewireless communication module 592 may identify and authenticate theelectronic device 501 in a communication network, such as the firstnetwork 598 or the second network 599, using subscriber information(e.g., international mobile subscriber identity (IMSI)) stored in thesubscriber identification module 596.

The antenna module 597 may transmit and/or receive a signal and/or powerto and/or from the outside (e.g., the external electronic device) of theelectronic device 501. According to one embodiment, the antenna module597 may include one or more antennas, and, therefrom, at least oneantenna appropriate for a communication scheme used in the communicationnetwork, such as the first network 598 and/or the second network 599,may be selected, for example, by the communication module 590 (e.g., thewireless communication module 592). The signal and/or the power may thenbe transmitted and/or received between the communication module 590 andthe external electronic device via the selected at least one antenna.

At least some of the above-described components may be mutually coupledand communicate signals (e.g., commands and/or data) therebetween via aninter-peripheral communication scheme (e.g., a bus, a general purposeinput and output (GPIO), a serial peripheral interface (SPI), and/or amobile industry processor interface (MIPI)).

According to one embodiment, commands and/or data may be transmittedand/or received between the electronic device 501 and the externalelectronic device 504 via the server 508 coupled with the second network599. Each of the electronic devices 502 and 504 may be a device of asame type as, or a different type from, the electronic device 501. Allor some of operations to be executed at or by the electronic device 501may be executed at one or more of the external electronic devices 502,504, or 508. For example, if the electronic device 501 should perform afunction and/or a service automatically, or in response to a requestfrom a user or another device, the electronic device 501, instead of, orin addition to, executing the function and/or the service, may requestthe one or more external electronic devices to perform at least a partof the function and/or the service. The one or more external electronicdevices receiving the request may perform the at least a part of thefunction and/or the service requested, and/or an additional functionand/or an additional service related to the request, and transfer anoutcome of the performing to the electronic device 501. The electronicdevice 501 may provide the outcome, with or without further processingof the outcome, as at least a part of a reply to the request. To thatend, a cloud computing, distributed computing, and/or client-servercomputing technology may be used, for example.

One embodiment may be implemented as software (e.g., the program 540)including one or more instructions that are stored in a storage medium(e.g., internal memory 536 or external memory 538) that is readable by amachine (e.g., the electronic device 501). For example, a processor ofthe electronic device 501 may invoke at least one of the one or moreinstructions stored in the storage medium, and execute it, with orwithout using one or more other components under the control of theprocessor. Thus, a machine may be operated to perform at least onefunction according to the at least one instruction invoked. The one ormore instructions may include code generated by a compiler or codeexecutable by an interpreter. A machine-readable storage medium may beprovided in the form of a non-transitory storage medium. The term“non-transitory” indicates that the storage medium is a tangible device,and does not include a signal (e.g., an electromagnetic wave), but thisterm does not differentiate between where data is semi-permanentlystored in the storage medium and where the data is temporarily stored inthe storage medium.

According to one embodiment, a method of the disclosure may be includedand provided in a computer program product. The computer program productmay be traded as a product between a seller and a buyer. The computerprogram product may be distributed in the form of a machine-readablestorage medium (e.g., a compact disc read only memory (CD-ROM)), or bedistributed (e.g., downloaded or uploaded) online via an applicationstore (e.g., Play Store™), or between two user devices (e.g., smartphones) directly. If distributed online, at least part of the computerprogram product may be temporarily generated or at least temporarilystored in the machine-readable storage medium, such as memory of themanufacturer's server, a server of the application store, or a relayserver.

Herein, embodiments of the present disclosure are described in detailwith reference to the accompanying drawings. It should be noted thatsame or similar elements may be designated by the same referencenumerals/letters even though they are shown in different drawings. Inthe description herein, specific details such as detailed configurationsand components are provided to assist with the overall understanding ofthe embodiments of the present disclosure. Various changes andmodifications of the embodiments described herein may be made withoutdeparting from the scope of the present disclosure. Certain detaileddescriptions may be omitted for clarity and conciseness.

The present disclosure provides for various modifications and variousembodiments. It should be understood that the present disclosure is notlimited to the various embodiments explicitly described or detailedherein, and that the present disclosure includes modifications,equivalents, and alternatives within the scope of the presentdisclosure.

Although terms including an ordinal number such as first, second, etc.,may be used for describing various elements, the elements are notrestricted by such terms. Such terms are used to distinguish one elementfrom another element, and do not imply any specific ordering. As usedherein, the term “and/or” includes any and all combinations of one ormore associated items. Singular forms are intended to include pluralforms unless the context clearly indicates otherwise. In the presentdisclosure, it should be understood that the terms “include” or “have”indicate the existence of a feature, a number, a step, an operation, astructural element, a part, or a combination thereof, and do not excludethe existence or probability of the addition of one or more otherfeatures, numbers, steps, operations, structural elements, parts, orcombinations thereof.

According to one embodiment, at least one component (e.g., a manager, aset of processor-executable instructions, a program, or a module) of theabove-described components may include a single entity or multipleentities. One or more of the above-described components may be omitted,or one or more other components may be added. Alternatively oradditionally, a plurality of components (e.g., a manager, a set ofprocessor-executable instructions, a program, or a module) may beintegrated into a single component. In this case, the integratedcomponent may still perform one or more functions of each of theplurality of components in the same or similar manner as they areperformed by a corresponding one of the plurality of components beforethe integration. Operations performed by the manager, the set ofprocessor-executable instructions, the program, the module, or anothercomponent may be carried out sequentially, in parallel, repeatedly, orheuristically, or one or more of the operations may be executed in adifferent order or omitted, or one or more other operations may beadded.

What is claimed is:
 1. A method of optimizing at least one in-phase (I)and quadrature (Q) mismatch compensator (MC) parameter value for atleast one IQMC parameter of an IQMC, comprising: generating a set oftested IQMC candidate parameter values by performing an iterative methoduntil an exit condition is reached, at least one iteration of theiterative method comprising: selecting a first IQMC candidate parametervalue for the at least one parameter of the IQMC; determining, using thefirst IQMC candidate parameter value, a performance metric value thatcomprises at least one of (i) an image rejection ratio (IRR) value, (ii)a signal-to-interference-plus-noise ratio (SINR) value, or (iii) asignal-to-image ratio (SImR) value; and determining a second IQMCcandidate parameter value that is an update to the first IQMC candidateparameter value, wherein the set of tested IQMC test parameters includesat least the first IQMC candidate parameter value and the second IQMCcandidate parameter value, and determining an IQMC candidate parametervalue of the set of tested IQMC candidate parameter values thatoptimizes the performance metric.
 2. The method of claim 1, wherein theiterative method is a gradient ascent method or a gradient descentmethod, and the iterative method further comprises calculating agradient of the performance metric.
 3. The method of claim 2, whereinthe gradient ascent method or the gradient descent method uses a costfunction that is a function of the at least one IQMC parameter.
 4. Themethod of claim 3, wherein the cost function is further a function ofIQMM parameters corresponding to an up-conversion circuit or adown-conversion circuit that is connected, directly or indirectly, tothe IQMC.
 5. The method of claim 3, wherein the at least one IQMCparameter includes at least one tap weight of a filter of the IQMC. 6.The method of claim 5, wherein the IQMC comprises a real-valuedcompensator (RVC) that includes a multiplier, and the at least one IQMCparameter includes a real-valued cross-multiplication factor that isinput to the multiplier.
 7. The method of claim 1, wherein the IQMCcomprises a complex-valued compensator (CVC).
 8. The method of claim 1,wherein the IQMC comprises a real-valued compensator (RVC).
 9. Themethod of claim 1, wherein the performance metric value comprises theIRR value, and the IRR value is a minimum IRR value of a set of IRRvalues of the IQMC, the set of IRR values corresponding to differentfrequencies.
 10. The method of claim 1, wherein the performance metricvalue comprises the IRR value, and the IRR value is a minimum arithmeticor geometric mean of IRR values per carrier cell (CC) in a carrieraggregation (CA) scheme.
 11. A device configured to optimize at leastone in-phase (I) and quadrature (Q) mismatch compensator (MC) parametervalue for at least one IQMC parameter of an IQMC, comprising: aprocessor; and non-transitory processor-executable media storinginstructions that, when executed by the processor, cause the processorto: generate a set of tested IQMC candidate parameter values byperforming an iterative method until an exit condition is reached, atleast one iteration of the iterative method comprising: selecting afirst IQMC candidate parameter value for the at least one parameter ofthe IQMC; determining, using the first IQMC candidate parameter value, aperformance metric value that comprises at least one of (i) an imagerejection ratio (IRR) value, (ii) a signal-to-interference-plus-noiseratio (SINR) value, or (iii) a signal-to-image ratio (SImR) value; anddetermining a second IQMC candidate parameter value that is an update tothe first IQMC candidate parameter value, wherein the set of tested IQMCtest parameters includes at least the first IQMC candidate parametervalue and the second IQMC candidate parameter value, and determine anIQMC candidate parameter value of the set of tested IQMC candidateparameter values that optimizes the performance metric.
 12. The deviceof claim 11, wherein the iterative method is a gradient ascent method ora gradient descent method, and the iterative method further comprisescalculating a gradient of the performance metric.
 13. The device ofclaim 12, wherein the gradient ascent method or the gradient descentmethod uses a cost function that is a function of the at least one IQMCparameter.
 14. The device of claim 13, wherein the cost function isfurther a function of IQMM parameters corresponding to an up-conversioncircuit or a down-conversion circuit that is connected, directly orindirectly, to the IQMC.
 15. The device of claim 13, wherein the atleast one IQMC parameter includes at least one tap weight of a filter ofthe IQMC.
 16. The device of claim 15, wherein the IQMC comprises areal-valued compensator (RVC) that includes a multiplier, and the atleast one IQMC parameter includes a real-valued cross-multiplicationfactor that is input to the multiplier.
 17. The device of claim 11,wherein the IQMC comprises a complex-valued compensator (CVC).
 18. Thedevice of claim 11, wherein the IQMC comprises a real-valued compensator(RVC).
 19. The device of claim 11, wherein the performance metric valuecomprises the IRR value, and the IRR value is a minimum IRR value of aset of IRR values of the IQMC, the set of IRR values corresponding todifferent frequencies.
 20. The device of claim 11, wherein theperformance metric value comprises the IRR value, and the IRR value is aminimum arithmetic or geometric mean of IRR values per carrier cell (CC)in a carrier aggregation (CA) scheme.